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Annotating text: The complete guide to close reading

Annotating text: The complete guide to close reading

As students, researchers, and self-learners, we understand the power of reading and taking smart notes . But what happens when we combine those together? This is where annotating text comes in.

Annotated text is a written piece that includes additional notes and commentary from the reader. These notes can be about anything from the author's style and tone to the main themes of the work. By providing context and personal reactions, annotations can turn a dry text into a lively conversation.

Creating text annotations during close readings can help you follow the author's argument or thesis and make it easier to find critical points and supporting evidence. Plus, annotating your own texts in your own words helps you to better understand and remember what you read.

This guide will take a closer look at annotating text, discuss why it's useful, and how you can apply a few helpful strategies to develop your annotating system.

What does annotating text mean?

Annotating text: yellow pen and a yellow notebook

Text annotation refers to adding notes, highlights, or comments to a text. This can be done using a physical copy in textbooks or printable texts. Or you can annotate digitally through an online document or e-reader.

Generally speaking, annotating text allows readers to interact with the content on a deeper level, engaging with the material in a way that goes beyond simply reading it. There are different levels of annotation, but all annotations should aim to do one or more of the following:

  • Summarize the key points of the text
  • Identify evidence or important examples
  • Make connections to other texts or ideas
  • Think critically about the author's argument
  • Make predictions about what might happen next

When done effectively, annotation can significantly improve your understanding of a text and your ability to remember what you have read.

What are the benefits of annotation?

There are many reasons why someone might wish to annotate a document. It's commonly used as a study strategy and is often taught in English Language Arts (ELA) classes. Students are taught how to annotate texts during close readings to identify key points, evidence, and main ideas.

In addition, this reading strategy is also used by those who are researching for self-learning or professional growth. Annotating texts can help you keep track of what you’ve read and identify the parts most relevant to your needs. Even reading for pleasure can benefit from annotation, as it allows you to keep track of things you might want to remember or add to your personal knowledge management system .

Annotating has many benefits, regardless of your level of expertise. When you annotate, you're actively engaging with the text, which can help you better understand and learn new things . Additionally, annotating can save you time by allowing you to identify the most essential points of a text before starting a close reading or in-depth analysis.

There are few studies directly on annotation, but the body of research is growing. In one 2022 study, specific annotation strategies increased student comprehension , engagement, and academic achievement. Students who annotated read slower, which helped them break down texts and visualize key points. This helped students focus, think critically , and discuss complex content.

Annotation can also be helpful because it:

  • Allows you to quickly refer back to important points in the text without rereading the entire thing
  • Helps you to make connections between different texts and ideas
  • Serves as a study aid when preparing for exams or writing essays
  • Identifies gaps in your understanding so that you can go back and fill them in

The process of annotating text can make your reading experience more fruitful. Adding comments, questions, and associations directly to the text makes the reading process more active and enjoyable.

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How do you annotate text?

2 pens and 2 notebooks

There are many different ways to annotate while reading. The traditional method of annotating uses highlighters, markers, and pens to underline, highlight, and write notes in paper books. Modern methods have now gone digital with apps and software. You can annotate on many note-taking apps, as well as online documents like Google Docs.

While there are documented benefits of handwritten notes, recent research shows that digital methods are effective as well. Among college students in an introductory college writing course, those with more highlighting on digital texts correlated with better reading comprehension than those with more highlighted sections on paper.

No matter what method you choose, the goal is always to make your reading experience more active, engaging, and productive. To do so, the process can be broken down into three simple steps:

  • Do the first read-through without annotating to get a general understanding of the material.
  • Reread the text and annotate key points, evidence, and main ideas.
  • Review your annotations to deepen your understanding of the text.

Of course, there are different levels of annotation, and you may only need to do some of the three steps. For example, if you're reading for pleasure, you might only annotate key points and passages that strike you as interesting or important. Alternatively, if you're trying to simplify complex information in a detailed text, you might annotate more extensively.

The type of annotation you choose depends on your goals and preferences. The key is to create a plan that works for you and stick with it.

Annotation strategies to try

When annotating text, you can use a variety of strategies. The best method for you will depend on the text itself, your reason for reading, and your personal preferences. Start with one of these common strategies if you don't know where to begin.

  • Questioning: As you read, note any questions that come to mind as you engage in critical thinking . These could be questions about the author's argument, the evidence they use, or the implications of their ideas.
  • Summarizing: Write a brief summary of the main points after each section or chapter. This is a great way to check your understanding, help you process information , and identify essential information to reference later.
  • Paraphrasing: In addition to (or instead of) summaries, try paraphrasing key points in your own words. This will help you better understand the material and make it easier to reference later.
  • Connecting: Look for connections between different parts of the text or other ideas as you read. These could be things like similarities, contrasts, or implications. Make a note of these connections so that you can easily reference them later.
  • Visualizing: Sometimes, it can be helpful to annotate text visually by drawing pictures or taking visual notes . This can be especially helpful when trying to make connections between different ideas.
  • Responding: Another way to annotate is to jot down your thoughts and reactions as you read. This can be a great way to personally engage with the material and identify any areas you need clarification on.

Combining the three-step annotation process with one or more strategies can create a customized, powerful reading experience tailored to your specific needs.

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7 tips for effective annotations

HIGHLIGHT spelled using letter tiles

Once you've gotten the hang of the annotating process and know which strategies you'd like to use, there are a few general tips you can follow to make the annotation process even more effective.

1. Read with a purpose. Before you start annotating, take a moment to consider what you're hoping to get out of the text. Do you want to gain a general overview? Are you looking for specific information? Once you know what you're looking for, you can tailor your annotations accordingly.

2. Be concise. When annotating text, keep it brief and focus on the most important points. Otherwise, you risk annotating too much, which can feel a bit overwhelming, like having too many tabs open . Limit yourself to just a few annotations per page until you get a feel for what works for you.

3. Use abbreviations and symbols. You can use abbreviations and symbols to save time and space when annotating digitally. If annotating on paper, you can use similar abbreviations or symbols or write in the margins. For example, you might use ampersands, plus signs, or question marks.

4. Highlight or underline key points. Use highlighting or underlining to draw attention to significant passages in the text. This can be especially helpful when reviewing a text for an exam or essay. Try using different colors for each read-through or to signify different meanings.

5. Be specific. Vague annotations aren't very helpful. Make sure your note-taking is clear and straightforward so you can easily refer to them later. This may mean including specific inferences, key points, or questions in your annotations.

6. Connect ideas. When reading, you'll likely encounter ideas that connect to things you already know. When these connections occur, make a note of them. Use symbols or even sticky notes to connect ideas across pages. Annotating this way can help you see the text in a new light and make connections that you might not have otherwise considered.

7. Write in your own words. When annotating, copying what the author says verbatim can be tempting. However, it's more helpful to write, summarize or paraphrase in your own words. This will force you to engage your information processing system and gain a deeper understanding.

These tips can help you annotate more effectively and get the most out of your reading. However, it’s important to remember that, just like self-learning , there is no one "right" way to annotate. The process is meant to enrich your reading comprehension and deepen your understanding, which is highly individual. Most importantly, your annotating system should be helpful and meaningful for you.

Engage your learning like never before by learning how to annotate text

Learning to effectively annotate text is a powerful tool that can improve your reading, self-learning , and study strategies. Using an annotating system that includes text annotations and note-taking during close reading helps you actively engage with the text, leading to a deeper understanding of the material.

Try out different annotation strategies and find what works best for you. With practice, annotating will become second nature and you'll reap all the benefits this powerful tool offers.

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How to Annotate Texts

Use the links below to jump directly to any section of this guide:

Annotation Fundamentals

How to start annotating , how to annotate digital texts, how to annotate a textbook, how to annotate a scholarly article or book, how to annotate literature, how to annotate images, videos, and performances, additional resources for teachers.

Writing in your books can make you smarter. Or, at least (according to education experts), annotation–an umbrella term for underlining, highlighting, circling, and, most importantly, leaving comments in the margins–helps students to remember and comprehend what they read. Annotation is like a conversation between reader and text. Proper annotation allows students to record their own opinions and reactions, which can serve as the inspiration for research questions and theses. So, whether you're reading a novel, poem, news article, or science textbook, taking notes along the way can give you an advantage in preparing for tests or writing essays. This guide contains resources that explain the benefits of annotating texts, provide annotation tools, and suggest approaches for diverse kinds of texts; the last section includes lesson plans and exercises for teachers.

Why annotate? As the resources below explain, annotation allows students to emphasize connections to material covered elsewhere in the text (or in other texts), material covered previously in the course, or material covered in lectures and discussion. In other words, proper annotation is an organizing tool and a time saver. The links in this section will introduce you to the theory, practice, and purpose of annotation. 

How to Mark a Book, by Mortimer Adler

This famous, charming essay lays out the case for marking up books, and provides practical suggestions at the end including underlining, highlighting, circling key words, using vertical lines to mark shifts in tone/subject, numbering points in an argument, and keeping track of questions that occur to you as you read. 

How Annotation Reshapes Student Thinking (TeacherHUB)

In this article, a high school teacher discusses the importance of annotation and how annotation encourages more effective critical thinking.

The Future of Annotation (Journal of Business and Technical Communication)

This scholarly article summarizes research on the benefits of annotation in the classroom and in business. It also discusses how technology and digital texts might affect the future of annotation. 

Annotating to Deepen Understanding (Texas Education Agency)

This website provides another introduction to annotation (designed for 11th graders). It includes a helpful section that teaches students how to annotate reading comprehension passages on tests.

Once you understand what annotation is, you're ready to begin. But what tools do you need? How do you prepare? The resources linked in this section list strategies and techniques you can use to start annotating. 

What is Annotating? (Charleston County School District)

This resource gives an overview of annotation styles, including useful shorthands and symbols. This is a good place for a student who has never annotated before to begin.

How to Annotate Text While Reading (YouTube)

This video tutorial (appropriate for grades 6–10) explains the basic ins and outs of annotation and gives examples of the type of information students should be looking for.

Annotation Practices: Reading a Play-text vs. Watching Film (U Calgary)

This blog post, written by a student, talks about how the goals and approaches of annotation might change depending on the type of text or performance being observed. 

Annotating Texts with Sticky Notes (Lyndhurst Schools)

Sometimes students are asked to annotate books they don't own or can't write in for other reasons. This resource provides some strategies for using sticky notes instead.

Teaching Students to Close Read...When You Can't Mark the Text (Performing in Education)

Here, a sixth grade teacher demonstrates the strategies she uses for getting her students to annotate with sticky notes. This resource includes a link to the teacher's free Annotation Bookmark (via Teachers Pay Teachers).

Digital texts can present a special challenge when it comes to annotation; emerging research suggests that many students struggle to critically read and retain information from digital texts. However, proper annotation can solve the problem. This section contains links to the most highly-utilized platforms for electronic annotation.

Evernote is one of the two big players in the "digital annotation apps" game. In addition to allowing users to annotate digital documents, the service (for a fee) allows users to group multiple formats (PDF, webpages, scanned hand-written notes) into separate notebooks, create voice recordings, and sync across all sorts of devices. 

OneNote is Evernote's main competitor. Reviews suggest that OneNote allows for more freedom for digital note-taking than Evernote, but that it is slightly more awkward to import and annotate a PDF, especially on certain platforms. However, OneNote's free version is slightly more feature-filled, and OneNote allows you to link your notes to time stamps on an audio recording.

Diigo is a basic browser extension that allows a user to annotate webpages. Diigo also offers a Screenshot app that allows for direct saving to Google Drive.

While the creators of Hypothesis like to focus on their app's social dimension, students are more likely to be interested in the private highlighting and annotating functions of this program.

Foxit PDF Reader

Foxit is one of the leading PDF readers. Though the full suite must be purchased, Foxit offers a number of annotation and highlighting tools for free.

Nitro PDF Reader

This is another well-reviewed, free PDF reader that includes annotation and highlighting. Annotation, text editing, and other tools are included in the free version.

Goodreader is a very popular Mac-only app that includes annotation and editing tools for PDFs, Word documents, Powerpoint, and other formats.

Although textbooks have vocabulary lists, summaries, and other features to emphasize important material, annotation can allow students to process information and discover their own connections. This section links to guides and video tutorials that introduce you to textbook annotation. 

Annotating Textbooks (Niagara University)

This PDF provides a basic introduction as well as strategies including focusing on main ideas, working by section or chapter, annotating in your own words, and turning section headings into questions.

A Simple Guide to Text Annotation (Catawba College)

The simple, practical strategies laid out in this step-by-step guide will help students learn how to break down chapters in their textbooks using main ideas, definitions, lists, summaries, and potential test questions.

Annotating (Mercer Community College)

This packet, an excerpt from a literature textbook, provides a short exercise and some examples of how to do textbook annotation, including using shorthand and symbols.

Reading Your Healthcare Textbook: Annotation (Saddleback College)

This powerpoint contains a number of helpful suggestions, especially for students who are new to annotation. It emphasizes limited highlighting, lots of student writing, and using key words to find the most important information in a textbook. Despite the title, it is useful to a student in any discipline.

Annotating a Textbook (Excelsior College OWL)

This video (with included transcript) discusses how to use textbook features like boxes and sidebars to help guide annotation. It's an extremely helpful, detailed discussion of how textbooks are organized.

Because scholarly articles and books have complex arguments and often depend on technical vocabulary, they present particular challenges for an annotating student. The resources in this section help students get to the heart of scholarly texts in order to annotate and, by extension, understand the reading.

Annotating a Text (Hunter College)

This resource is designed for college students and shows how to annotate a scholarly article using highlighting, paraphrase, a descriptive outline, and a two-margin approach. It ends with a sample passage marked up using the strategies provided. 

Guide to Annotating the Scholarly Article (ReadWriteThink.org)

This is an effective introduction to annotating scholarly articles across all disciplines. This resource encourages students to break down how the article uses primary and secondary sources and to annotate the types of arguments and persuasive strategies (synthesis, analysis, compare/contrast).

How to Highlight and Annotate Your Research Articles (CHHS Media Center)

This video, developed by a high school media specialist, provides an effective beginner-level introduction to annotating research articles. 

How to Read a Scholarly Book (AndrewJacobs.org)

In this essay, a college professor lets readers in on the secrets of scholarly monographs. Though he does not discuss annotation, he explains how to find a scholarly book's thesis, methodology, and often even a brief literature review in the introduction. This is a key place for students to focus when creating annotations. 

A 5-step Approach to Reading Scholarly Literature and Taking Notes (Heather Young Leslie)

This resource, written by a professor of anthropology, is an even more comprehensive and detailed guide to reading scholarly literature. Combining the annotation techniques above with the reading strategy here allows students to process scholarly book efficiently. 

Annotation is also an important part of close reading works of literature. Annotating helps students recognize symbolism, double meanings, and other literary devices. These resources provide additional guidelines on annotating literature.

AP English Language Annotation Guide (YouTube)

In this ~10 minute video, an AP Language teacher provides tips and suggestions for using annotations to point out rhetorical strategies and other important information.

Annotating Text Lesson (YouTube)

In this video tutorial, an English teacher shows how she uses the white board to guide students through annotation and close reading. This resource uses an in-depth example to model annotation step-by-step.

Close Reading a Text and Avoiding Pitfalls (Purdue OWL)

This resources demonstrates how annotation is a central part of a solid close reading strategy; it also lists common mistakes to avoid in the annotation process.

AP Literature Assignment: Annotating Literature (Mount Notre Dame H.S.)

This brief assignment sheet contains suggestions for what to annotate in a novel, including building connections between parts of the book, among multiple books you are reading/have read, and between the book and your own experience. It also includes samples of quality annotations.

AP Handout: Annotation Guide (Covington Catholic H.S.)

This annotation guide shows how to keep track of symbolism, figurative language, and other devices in a novel using a highlighter, a pencil, and every part of a book (including the front and back covers).

In addition to written resources, it's possible to annotate visual "texts" like theatrical performances, movies, sculptures, and paintings. Taking notes on visual texts allows students to recall details after viewing a resource which, unlike a book, can't be re-read or re-visited ( for example, a play that has finished its run, or an art exhibition that is far away). These resources draw attention to the special questions and techniques that students should use when dealing with visual texts.

How to Take Notes on Videos (U of Southern California)

This resource is a good place to start for a student who has never had to take notes on film before. It briefly outlines three general approaches to note-taking on a film. 

How to Analyze a Movie, Step-by-Step (San Diego Film Festival)

This detailed guide provides lots of tips for film criticism and analysis. It contains a list of specific questions to ask with respect to plot, character development, direction, musical score, cinematography, special effects, and more. 

How to "Read" a Film (UPenn)

This resource provides an academic perspective on the art of annotating and analyzing a film. Like other resources, it provides students a checklist of things to watch out for as they watch the film.

Art Annotation Guide (Gosford Hill School)

This resource focuses on how to annotate a piece of art with respect to its formal elements like line, tone, mood, and composition. It contains a number of helpful questions and relevant examples. 

Photography Annotation (Arts at Trinity)

This resource is designed specifically for photography students. Like some of the other resources on this list, it primarily focuses on formal elements, but also shows students how to integrate the specific technical vocabulary of modern photography. This resource also contains a number of helpful sample annotations.

How to Review a Play (U of Wisconsin)

This resource from the University of Wisconsin Writing Center is designed to help students write a review of a play. It contains suggested questions for students to keep in mind as they watch a given production. This resource helps students think about staging, props, script alterations, and many other key elements of a performance.

This section contains links to lessons plans and exercises suitable for high school and college instructors.

Beyond the Yellow Highlighter: Teaching Annotation Skills to Improve Reading Comprehension (English Journal)

In this journal article, a high school teacher talks about her approach to teaching annotation. This article makes a clear distinction between annotation and mere highlighting.

Lesson Plan for Teaching Annotation, Grades 9–12 (readwritethink.org)

This lesson plan, published by the National Council of Teachers of English, contains four complete lessons that help introduce high school students to annotation.

Teaching Theme Using Close Reading (Performing in Education)

This lesson plan was developed by a middle school teacher, and is aligned to Common Core. The teacher presents her strategies and resources in comprehensive fashion.

Analyzing a Speech Using Annotation (UNC-TV/PBS Learning Media)

This complete lesson plan, which includes a guide for the teacher and relevant handouts for students, will prepare students to analyze both the written and presentation components of a speech. This lesson plan is best for students in 6th–10th grade.

Writing to Learn History: Annotation and Mini-Writes (teachinghistory.org)

This teaching guide, developed for high school History classes, provides handouts and suggested exercises that can help students become more comfortable with annotating historical sources.

Writing About Art (The College Board)

This Prezi presentation is useful to any teacher introducing students to the basics of annotating art. The presentation covers annotating for both formal elements and historical/cultural significance.

Film Study Worksheets (TeachWithMovies.org)

This resource contains links to a general film study worksheet, as well as specific worksheets for novel adaptations, historical films, documentaries, and more. These resources are appropriate for advanced middle school students and some high school students. 

Annotation Practice Worksheet (La Guardia Community College)

This worksheet has a sample text and instructions for students to annotate it. It is a useful resource for teachers who want to give their students a chance to practice, but don't have the time to select an appropriate piece of text. 

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Labellerr

The Ultimate Guide to Text Annotation: Techniques, Tools, and Best Practices

Puneet Jindal

Puneet Jindal

Introduction.

Welcome to the realm where language meets machine intelligence : text annotation - the catalyst propelling artificial intelligence to understand, interpret, and communicate in human language. Evolving from editorial footnotes to a cornerstone in data science, text annotation now drives Natural Language Processing (NLP) and Computer Vision , reshaping industries across the globe.

Imagine AI models decoding sentiments, recognizing entities, and grasping human nuances in a text. Text annotation is the magical key to making this possible. Join us on this journey through text annotation - exploring its techniques, challenges, and the transformative potential it holds for healthcare, finance, government, logistics, and beyond.

In this exploration, witness text annotation's evolution and its pivotal role in fueling AI's understanding of language. Explore how tools such as Labellerr help in text annotation and work.  Let's unravel the artistry behind text annotation, shaping a future where AI comprehends, adapts, and innovates alongside human communication.

1. What is Text Annotation?

Text annotation is a crucial process that involves adding labels, comments, or metadata to textual data to facilitate machine learning algorithms' understanding and analysis.

This practice, known for its traditional role in editorial reviews by adding comments or footnotes to text drafts, has evolved significantly within the realm of data science, particularly in Natural Language Processing (NLP) and Computer Vision applications .

In the context of machine learning, text annotation takes on a more specific role. It involves systematically labeling pieces of text to create a reference dataset, enabling supervised machine learning algorithms to recognize patterns, learn from labeled data, and make accurate predictions or classifications when faced with new, unseen text.

To elaborate on what it means to annotate text: In data science and NLP, annotating text demands a comprehensive understanding of the problem domain and the dataset. It involves identifying and marking relevant features within the text. This can be akin to labeling images in image classification tasks, but in text, it includes categorizing sentences or segments into predefined classes or topics.

For instance, labeling sentiments in online reviews, distinguishing fake and real news articles, or marking parts of speech and named entities in text.

text annotation

1.1 Text Annotation Tasks: A Multifaceted Approach to Data Labeling

(i) Text Classification : Assigning predefined categories or labels to text segments based on their content, such as sentiment analysis or topic classification.

(ii) Named Entity Recognition (NER) : Identifying and labeling specific entities within the text, like names of people, organizations, locations, dates, etc.

(iii) Parts of Speech Tagging : Labeling words in a sentence with their respective grammatical categories, like nouns, verbs, adjectives, etc.

(iv) Summarization : Condensing a lengthy text into a shorter, coherent version while retaining its key information.

1.2 Significant Benefits of Text Annotation

(i) Improved Machine Learning Models : Annotated data provides labeled examples for algorithms to learn from, enhancing their ability to make accurate predictions or classifications when faced with new, unlabeled text.

(ii) Enhanced Performance and Efficiency : Annotations expedite the learning process by offering clear indicators to algorithms, leading to improved performance and faster model convergence.

(iii) Nuance Recognition : Text annotations help algorithms understand contextual nuances, sarcasm, or subtle linguistic cues that might not be immediately apparent, enhancing their ability to interpret text accurately.

(iv) Applications in Various Industries : Text annotation is vital across industries, aiding in tasks like content moderation, sentiment analysis for customer feedback , information extraction for search engines , and much more.

Text annotation is a critical process in modern machine learning, empowering algorithms to comprehend, interpret, and extract valuable insights from textual data, thereby enabling various applications across different sectors.

2. Types of Text Annotation

Text Annotation Types

Text annotation, in the realm of data labeling and Natural Language Processing (NLP), encompasses a diverse range of techniques used to label, categorize, and extract meaningful information from textual data. This multifaceted process involves several types of annotations, each serving a distinct purpose in enhancing machine understanding and analysis of text.

Types of Text Annotation

These annotation types include sentiment annotation, intent annotation, entity annotation, text classification, linguistic annotation, named entity recognition (NER), part-of-speech tagging, keyphrase tagging, entity linking, document classification, language identification, and toxicity classification.

1. Sentiment Annotation

Sentiment annotation is a technique crucial for understanding emotions conveyed in text. Assigning sentiments like positive, negative, or neutral to sentences aids in sentiment analysis .

This process involves deciphering emotions in customer reviews on e-commerce platforms (e.g., Amazon, Flipkart), enabling businesses to gauge customer satisfaction.

Precise sentiment annotation is vital for training machine learning models that categorize texts into various emotions, facilitating a deeper understanding of user sentiments towards products or services.

Let's consider various instances where sentiment annotation encounters complexities:

Sentiment Annotation

(i) Clear Emotions: In the initial examples, emotions are distinctly evident. The first instance exudes happiness and positivity, while the second reflects disappointment and negative feelings. However, in the third case, emotions become intricate. Phrases like "nostalgic" or "bittersweet" evoke mixed sentiments, making it challenging to classify into a single emotion.

(ii) Success versus Failure: Analyzing phrases such as "Yay! Argentina beat France in the World Cup Finale" presents a paradox. Initially appearing positive, this sentence also implies negative emotions for the opposing side, complicating straightforward sentiment classification.

(iii) Sarcasm and Ridicule: Capturing sarcasm involves comprehending nuanced human communication styles, relying on context, tone, and social cues—characteristics often intricate for machines to interpret.

(iv) Rhetorical Questions: Phrases like "Why do we have to quibble every time?" may seem neutral initially. However, the speaker's tone and delivery convey a sense of frustration and negativity, posing challenges in categorizing the sentiment accurately.

(v) Quoting or Re-tweeting: Sentiment annotation confronts difficulties when dealing with quoted or retweeted content. The sentiment expressed might not align with the opinions of the one sharing the quote, creating discrepancies in sentiment classification.

In essence, sentiment annotation encounters challenges due to the complexity of human emotions, contextual nuances, and the subtleties of language expression, making accurate classification a demanding task for automated systems.

Intent Annotation

Intent annotation is a crucial aspect in the development of chatbots and virtual assistants , forming the backbone of their functionality. It involves labeling or categorizing user messages or sentences to identify the underlying purpose or intention behind the communication.

This annotation process aims to understand and extract the user's intent, enabling these AI systems to provide contextually relevant and accurate responses. Intent annotation involves labeling sentences to discern the user's intention behind a message. By annotating intents like greetings, complaints, or inquiries, systems can generate appropriate responses.

Intent Annotation

Key points regarding intent text annotation include:

Purpose Identification: Intent annotation involves categorizing user messages into specific intents such as greetings, inquiries, complaints, feedback, orders, or any other actionable user intents. Each category represents a different user goal or purpose within the conversation.

Training Data Creation: Creating labeled datasets is crucial for training machine learning models to recognize and classify intents accurately. Annotated datasets consist of labeled sentences or phrases paired with their corresponding intended purposes, forming the foundation for model training.

Contextual Understanding: Intent annotation often requires a deep understanding of contextual nuances within language. It's not solely about identifying keywords but comprehending the broader meaning and context of user queries or statements.

Natural Language Understanding (NLU) : It falls under the realm of natural language processing (NLP) and requires sophisticated algorithms capable of interpreting and categorizing user intents accurately. Machine learning models, such as classifiers or neural networks, are commonly used for this purpose.

Iterative Process: Annotation of intents often involves an iterative process. Initially, a set of intent categories is defined based on common user interactions. As the system encounters new user intents, the annotation process may expand or refine these categories to ensure comprehensive coverage.

Quality Assurance and Validation: It's essential to validate and ensure the quality of labeled data. This may involve multiple annotators labeling the same data independently to assess inter-annotator agreement and enhance annotation consistency.

Adaptation and Evolution: Intent annotation isn't a one-time task. As user behaviors, language use, and interaction patterns evolve, the annotated intents also need periodic review and adaptation to maintain accuracy and relevance.

Enhancing User Experience: Accurate intent annotation is pivotal in enhancing user experience. It enables chatbots and virtual assistants to understand user needs promptly and respond with relevant and helpful information or actions, improving overall user satisfaction.

Industry-Specific Customization: Intent annotation can be industry-specific. For instance, in healthcare, intents may include appointment scheduling, medication queries, or symptom descriptions, while in finance, intents may revolve around account inquiries, transaction history, or support requests.

Continuous Improvement: Feedback loops and analytics derived from user interactions help refine intent annotation. Analyzing user feedback on system responses can drive improvements in intent categorization and response generation.

For instance, Siri or Alexa, trained on annotated data for specific intents, responds accurately to user queries, enhancing user experience. Below are given examples:

  • Greeting Intent: Hello there, how are you?
  • Complaint Intent:  I am very disappointed with the service I received.
  • Inquiry Intent: What are your business hours?
  • Confirmation Intent:  Yes, I'd like to confirm my appointment for tomorrow at 10 AM.
  • Request Intent: Could you please provide me with the menu?
  • Gratitude Intent: Thank you so much for your help!
  • Feedback Intent:  I wanted to give feedback about the recent product purchase.
  • Apology Intent:  I'm sorry for the inconvenience caused.
  • Assistance Intent:  Can you assist me with setting up my account?
  • Goodbye Intent:  Goodbye, have a great day!

These annotations serve as training data for AI models to learn and understand different user intentions, enabling chatbots or virtual assistants to respond accurately and effectively.

Entity Annotation:

Entity annotation focuses on labeling key phrases, named entities, or parts of speech in text. This technique emphasizes crucial details in lengthy texts and aids in training models for entity extraction. Named entity recognition (NER) is a subset of entity annotation, labeling entities like people's names, locations, dates, etc., enabling machines to comprehend text more comprehensively by distinguishing semantic meanings.

Text Classification

Text classification assigns categories or labels to text segments. This annotation technique is essential for organizing text data into specific classes or topics, such as document classification or sentiment analysis. Categorizing tweets into education, politics, etc., helps organize content and enables better understanding.

Text Classification

Let's look at each of these forms separately.

Document Classification: This involves assigning a single label to a document, aiding in the efficient sorting of vast textual data based on its primary theme or content.

Product Categorization: It's the process of organizing products or services into specific classes or categories. This helps enhance search results in eCommerce platforms, improving SEO strategies and boosting visibility in product ranking pages.

Email Classification: This task involves categorizing emails into either spam or non-spam (ham) categories, typically based on their content, aiding in email filtering and prioritization.

News Article Classification: Categorizing news articles based on their content or topics such as politics, entertainment, sports, technology, etc. This categorization assists in better organizing and presenting news content to readers.

Language Identification: This task involves determining the language used in a given text, is useful in multilingual contexts or language-specific applications.

Toxicity Classification: Identifying whether a social media comment or post contains toxic content, hate speech, or is non-toxic. This classification helps in content moderation and creating safer online environments.

Each form of text annotation serves a specific purpose, enabling better organization, classification, and understanding of textual data, and contributing to various applications across industries and domains.

Linguistic Annotation

Linguistic annotation focuses on language-related details in text or speech, including semantics, phonetics, and discourse. It encompasses intonation, stress, pauses, and discourse relations. It helps systems understand linguistic nuances, like coreference resolution linking pronouns to their antecedents, semantic labeling, and annotating stress or tone in speech.

Named Entity Recognition (NER)

NER identifies and labels named entities like people's names, locations, dates, etc., in text. It plays a pivotal role in NLP applications, allowing systems like Google Translate or Siri to understand and process textual data accurately.

Part-of-Speech Tagging

Part-of-speech tagging labels words in a sentence with their grammatical categories (nouns, verbs, adjectives). It assists in parsing sentences and understanding their structure.

Keyphrase Tagging

Keyphrase tagging locates and labels keywords or keyphrases in text, aiding in tasks like summarization or extracting key concepts from large text documents.

Entity Linking

Entity linking maps words in text to entities in a knowledge base, aiding in disambiguating entities' meanings and connecting them to larger datasets for contextual understanding.

3. Text Annotation use cases

(i) healthcare.

Text annotation significantly transforms healthcare operations by leveraging AI and machine learning techniques to enhance patient care, streamline processes, and improve overall efficiency:

Automatic Data Extraction: Text annotation aids in extracting critical information from clinical trial records, facilitating better access and analysis of medical documents. It expedites research efforts and supports comprehensive data-driven insights.

Patient Record Analysis: Annotated data enables thorough analysis of patient records, leading to improved outcomes and more accurate medical condition detection. It aids healthcare professionals in making informed decisions and providing tailored treatments.

Insurance Claims Processing: Within healthcare insurance, text annotation helps recognize medically insured patients, identify loss amounts, and extract policyholder information. This speeds up claims processing, ensuring faster service delivery to policyholders.

Healthcare Text Annotation

(II) Insurance

Text annotation in the insurance industry revolutionizes various facets of operations, making tasks more efficient and accurate:

Risk Evaluation: By annotating and extracting contextual data from contracts and forms, text annotation supports risk evaluation, enabling insurance companies to make more informed decisions while minimizing potential risks.

Claims Processing: Annotated data assists in recognizing entities like involved parties and loss amounts, significantly expediting the claims processing workflow. It aids in detecting dubious claims, contributing to fraud detection efforts.

Fraud Detection: Through text annotation, insurance firms can monitor and analyze documents and forms more effectively, enhancing their capabilities to detect fraudulent claims and irregularities.

Roboflow

(III) Banking

The banking sector utilizes text annotation to revolutionize operations and ensure better accuracy and customer satisfaction:

Fraud Identification: Text annotation techniques aid in identifying potential fraud and money laundering patterns, allowing banks to take proactive measures and ensure security.

Custom Data Extraction: Annotated text facilitates the extraction of critical information from contracts, improving workflows and ensuring compliance. It enables efficient data extraction for various attributes like loan rates and credit scores, supporting compliance monitoring.

banking text annotation

(IV) Government

In government operations, text annotation facilitates various tasks, ensuring better efficiency and compliance:

Regulatory Compliance: Text annotation streamlines financial operations by ensuring regulatory compliance through advanced analytics . It helps maintain compliance standards more effectively.

Document Classification: Through text classification and annotation, different types of legal cases can be categorized, ensuring efficient document management and access to digital documents.

Fraud Detection & Analytics: Text annotation assists in the early detection of fraudulent activities by utilizing linguistic annotation, semantic annotation, tone detection , and entity recognition. It enables analytics on vast amounts of data for insights.

Govt text annotation

(V) Logistics

Text annotation in logistics plays a pivotal role in handling massive volumes of data and improving customer experiences:

Invoice Annotation: Annotated text assists in extracting crucial details such as amounts, order numbers, and names from invoices. It streamlines billing and invoicing processes.

Customer Feedback Analysis: By utilizing sentiment and entity annotation, logistics companies can analyze customer feedback, ensuring better service improvements and customer satisfaction.

logistics text annotation

(VI) Media and News

Text annotation's role in the media industry is indispensable for content categorization and credibility:

Content Categorization: Annotation is crucial for categorizing news content into various segments such as sports, education, government, etc., enabling efficient content management and retrieval.

Entity Recognition: Annotating entities like names, locations, and key phrases in news articles aids in information retrieval and fact-checking. It contributes to credibility and accurate reporting.

Fake News Detection: Utilizing text annotation techniques such as NLP annotation and sentiment analysis enables the identification of fake news by analyzing the credibility and sentiment of the content.

media and news

These comprehensive applications across sectors showcase how text annotation significantly impacts various industries, making operations more efficient, accurate, and streamlined.

4. Text Annotation Guidelines

Annotation guidelines serve as a comprehensive set of instructions and rules for annotators when labeling or annotating text data for machine learning tasks. These guidelines are crucial as they define the objectives of the modeling task and the purpose behind the labels assigned to the data. They are crafted by a team familiar with the data and the intended use of the annotations.

Starting with defining the modeling problem and the desired outcomes, annotation guidelines cover various aspects:

(i) Annotation Techniques: Guidelines may start by choosing appropriate annotation methods tailored to the specific problem being addressed.

(ii) Case Definitions: They define common and potentially ambiguous cases that annotators might encounter in the data, along with instructions on how to handle each scenario.

(iii) Handling Ambiguity: Guidelines include examples from the data and strategies to deal with outliers, ambiguous instances, or unusual cases that might arise during annotation.

Text Annotation Workflow

An annotation workflow typically consists of several stages:

(i) Curating Annotation Guidelines: Define the problem, set the expected outcomes, and create comprehensive guidelines that are easy to follow and revisit.

(ii) Selecting a Labeling Tool: Choose appropriate text annotation tools, considering options like Labellerr or other available tools that suit the task's requirements.

(iii) Defining Annotation Process: Create a reproducible workflow that encompasses organizing data sources, utilizing guidelines, employing annotation tools effectively, documenting step-by-step annotation processes, defining formats for saving and exporting annotations, and reviewing each labeled sample.

(iv) Review and Quality Control: Regularly review labeled data to prevent generic label errors, biases, or inconsistencies. Multiple annotators may label the same samples to ensure consistency and reduce interpretational bias. Statistical measures like Cohen's kappa statistic can assess annotator agreement to identify and address discrepancies or biases in annotations.

Ensuring a streamlined flow of incoming data samples, rigorous review processes, and consistent adherence to annotation guidelines are crucial for generating high-quality labeled datasets for machine learning models. Regular monitoring and quality checks help maintain the reliability and integrity of the annotated data.

5. Text Annotation Tools and Technologies

Text Annotation Tools

Text annotation tools play a vital role in preparing data for AI and machine learning, particularly in natural language processing (NLP) applications. These tools fall into two main categories: open-source and commercial offerings. Open-source tools, available at no cost, are customizable and widely used in startups and academic projects for their affordability. Conversely, commercial tools offer advanced functionalities and support, making them suitable for large-scale and enterprise-level projects.

Commercial Text Annotation Tools

(i) labellerr.

Labellerr is a text annotation tool that provides high-quality and accurate text annotations for training AI models at scale. The tool, Labellerr, offers various features and services tailored to text annotation needs.

Labellerr Text Annotation

Labellerr boasts the following functionalities and services:

Text Annotation Features:

(i) Sentiment Analysis: Identifies sentiments and emotions in text, categorizing statements as positive, negative, or neutral.

(ii) Summarization: Highlights key sentences or phrases within text to create a summarized version.

(iii) Translation: Translates selected text segments into different languages, such as English to French or German to Italian.

(iv) Named-Entity Recognition: Tags named entities (e.g., ID, Name, Place, Price) in text based on predefined categories.

(v) Text Classification: Classifies text by assigning appropriate classes based on their content.

(vi) Question Answering: Matches questions with their respective answers to train models for generating accurate responses.

Automated Workflows:

(i) Customization: Allows users to create custom automated data workflows, collaborate in real-time, perform QA reviews, and gain complete visibility into AI operations.

(ii) Pipeline Management: Enables the creation and automation of text labeling workflows, multiple user roles, review cycles, inter-annotator agreements, and various annotation stages.

Text Labeling Services:

(i) Provides professional text annotators and linguists focused on ensuring quality and accuracy in annotations.

(ii) Offers fully managed services, allowing users to concentrate on other important aspects while delegating text annotation tasks.

Labellerr TA

Labellerr emerges as a comprehensive and versatile commercial text annotation tool that streamlines the process of annotating large text datasets for AI model training purposes. It provides a wide array of annotation capabilities and customizable workflows, catering to diverse text annotation requirements.

(II) SuperAnnotate

SuperAnnotate is an advanced text annotation tool designed to facilitate the creation of high-quality and accurate annotations essential for training top-performing AI models. This tool offers a wide array of features and functionalities aimed at streamlining text annotation processes for various industries and use cases.

SuperAnnotate

Key Features of SuperAnnotate's Text Annotation Tool:

Cloud Integrations: Supports integration with various cloud storage systems, allowing users to easily add items from their cloud repositories to the SuperAnnotate platform.

Versatile Use Cases: Encompasses all use cases, ensuring its applicability across different industries and scenarios.

Advanced Annotation Tools: Equipped with an array of advanced tools tailored for efficient text annotation.

Functionalities Offered by SuperAnnotate:

Sentiment Analysis: Capable of identifying sentiments expressed in text, determining whether statements are positive, negative, or neutral, and even detecting emotions like happiness or anger.

Summarization: Annotations can focus on key sentences or phrases within text, aiding in the creation of summarized versions.

Translation Assistance: Annotations assist in identifying elements for translation, such as sentences, terms, and specific entities.

Named-Entity Recognition: Detects and classifies named entities within text, sorting them into predefined categories like dates, locations, names of individuals, and more.

Text Classification: Assigns classes to texts based on their content and characteristics.

Question Answering: Enables the pairing of questions with corresponding answers to train models for generating accurate responses.

Efficiency-Boosting Features:

Token Annotation: Splits texts into units using linguistic knowledge, ensuring seamless and accurate annotation.

Classify All: Instantly assigns the same class to every occurrence of a word or phrase in a text, enhancing efficiency.

Quality-Focused Elements:

Collaboration System: Involves stakeholders in the quality review process through comments, fostering seamless collaboration and task distribution.

Status Tracking: Provides visibility into the status of items and projects, allowing users to track progress effectively.

Detailed Instructions: Sets a solid foundation for project execution by offering comprehensive project instructions to the team.

(III) V7 Labs

The V7 Text Annotation Tool is a feature within the V7 platform that facilitates the annotation of text data within images and documents. This tool automates the process of detecting and reading text from various types of visual content, including images, photos, documents, and videos.

v7 labs

Key features and steps associated with the V7 Text Annotation Tool include:

Text Scanner Model : V7 has incorporated a public Text Scanner model within its Neural Networks page. This model is designed to automatically detect and read text within images and documents.

Integration into Workflow : Add a model stage to the workflow under the Settings page of your dataset. Select the Text Scanner model from the dropdown list and map the newly created text class. If desired, enable the Auto-Start option to automatically process new images through the model at the beginning of the workflow.

Automatic Text Detection and Reading : Once set up, the V7 Text Annotation Tool will automatically scan and read text from different types of images, including documents, photos, and videos. The tool is extensively pre-trained, enabling it to interpret characters that might be challenging for humans to decipher accurately.

Overall, the V7 Text Annotation Tool streamlines the process of text annotation by leveraging a pre-trained model to automatically detect and read text within visual content, providing an efficient and accurate solution for handling text data in images and documents.

Open Source Text Annotation Tools

(i) piaf platform.

  • Led by Etalab, this tool aims to create a public Q&A dataset in French.
  • Initially designed for question/answer annotation, it allows users to write questions and highlight text segments that answer them.
  • Offers an easy installation process and collaborative annotation capabilities.
  • Export annotations in the format of the Stanford SQuAD dataset.
  • Limited to question/answer annotation but has potential for adaptation to other use cases like sentiment analysis or named entity recognition.

piaf platform

(II) Label Studio

  • Free and open-source tool suitable for various tasks like natural language processing, computer vision, and more.
  • Highly scalable and configurable labeling interface.
  • Provides templates for common tasks (sentiment analysis, named entities, object detection) for easy setup.
  • Allows exporting labeled data in multiple formats, compatible with learning algorithms.
  • Supports collaborative annotation and can be deployed on servers for simultaneous annotation by multiple collaborators.

Label studio

(III) Doccano

doccano

  • Originally designed for text annotation tasks and recently extended to image classification, object detection, and speech-to-text annotations.
  • Offers local installation via pip, supporting SQLite3 or PostgreSQL databases for saving annotations and datasets.
  • Docker image available for deployment on various cloud providers.
  • Simple user interface, collaborative features, and customizable labeling templates.
  • Allows importing datasets in various formats (CSV, JSON, fastText) and exporting annotations accordingly.

Doccano

These open-source tools provide valuable solutions for annotating text data, with each tool having its unique features and suitability for specific annotation tasks. While PIAF is focused on Q&A datasets in French, Label Studio offers extensive customization, and Doccano supports diverse annotation tasks, expanding beyond text to cover image and speech annotations.

Open-source NLP Service Toolkits

  • spaCy : A Python library designed for production-level NLP tasks. While not a standalone annotation tool, it's often used with tools like Prodigy or Doccano for text annotation.
  • NLTK (Natural Language Toolkit) : A popular Python platform that provides numerous text-processing libraries for various language-related tasks. It can be combined with other tools for text annotation purposes.
  • Stanford CoreNLP : A Java-based toolkit capable of performing diverse NLP tasks like part-of-speech tagging, named entity recognition, parsing, and coreference resolution. It's typically used as a backend for annotation tools.
  • GATE (General Architecture for Text Engineering) : An extensive open-source toolkit equipped with components for text processing, information extraction, and semantic annotation.
  • Apache OpenNLP : A machine learning-based toolkit supporting tasks such as tokenization, part-of-speech tagging, entity extraction, and more. It's used alongside other tools for text annotation.
  • UIMA (Unstructured Information Management Architecture) : An open-source framework facilitating the development of applications for analyzing unstructured information like text, audio, and video. It's used in conjunction with other tools for text annotation.

Commercial NLP Service Platforms

  • Amazon Comprehend : A machine learning-powered NLP service offering entity recognition, sentiment analysis, language detection, and other text insights. APIs facilitate easy integration into applications.
  • Google Cloud Natural Language API : Provides sentiment analysis, entity analysis, content classification, and other NLP features. Part of Google Cloud's Machine Learning APIs.
  • Microsoft Azure Text Analytics : Offers sentiment analysis, key phrase extraction, language detection, and named entity recognition among its text processing capabilities.
  • IBM Watson Natural Language Understanding : Utilizes deep learning to extract meaning, sentiment, entities, relations, and more from unstructured text. Available through IBM Cloud with REST APIs and SDKs for integration.
  • MeaningCloud : A text analytics platform supporting sentiment analysis, topic extraction, entity recognition, and classification across multiple languages through APIs and SDKs.
  • Rosette Text Analytics : Provides entity extraction, sentiment analysis, relationship extraction, and language identification functionalities across various languages. Can be integrated into applications using APIs and SDKs.

6. Challenges in Text Annotation

AI and ML companies face numerous hurdles in text annotation processes. These encompass ensuring data quality, efficiently handling large datasets, mitigating annotator biases, safeguarding sensitive information, and scaling operations as data volumes expand. Tackling these issues is crucial to achieving precise model training and robust AI outcomes.

Text Annotation challenges

(i) Ambiguity

This occurs when a word, phrase, or sentence holds multiple meanings, leading to inconsistencies in annotations. Resolving such ambiguities is vital for accurate machine learning model training. For instance, the phrase "I saw the man with the telescope" can be interpreted in different ways, impacting annotation accuracy.

(ii) Subjectivity

Annotating subjective language, containing personal opinions or emotions, poses challenges due to differing interpretations among annotators. Labeling sentiment in customer reviews can vary based on annotators' perceptions, resulting in inconsistencies in annotations.

(iii) Contextual Understanding

Accurate annotation relies on understanding the context in which words or phrases are used. Failing to consider context, such as the dual meaning of "bank" referring to a financial institution or a river side, can lead to incorrect annotations and hinder model performance.

(iv) Language Diversity

The need for proficiency in multiple languages poses challenges in annotating diverse datasets. Finding annotators proficient in less common languages or dialects is difficult, leading to inconsistencies in annotations and proficiency levels among annotators.

(v) Scalability

Annotating large volumes of data is time-consuming and resource-intensive. Handling increasing data volumes demands more annotators, posing challenges in efficiently scaling annotation efforts.

Hiring and training annotators and investing in annotation tools can be expensive. The significant investment required in the data labeling market emphasizes the challenge of balancing accurate annotations with the associated costs for AI and machine learning implementation.

7. The Future of Text Annotation

Text annotation, an integral part of data annotation, is experiencing several future trends that align with the broader advancements in data annotation processes. These trends are likely to shape the landscape of text annotation in the coming years:

Text Annotation Future

(i) Natural Language Processing (NLP) Advancements

With the rapid progress in NLP technologies, text annotation is expected to witness the development of more sophisticated tools that can understand and interpret textual data more accurately. This includes improvements in sentiment analysis, entity recognition, named entity recognition, and other text categorization tasks.

(ii) Contextual Understanding

Future trends in text annotation will likely focus on capturing contextual understanding within language models. This involves annotating text with a deeper understanding of nuances, tone, and context, leading to the creation of more context-aware and accurate language models.

(iii) Multilingual Annotation

As the demand for multilingual AI models grows, text annotation will follow suit. Future trends involve annotating and curating datasets in multiple languages, enabling the training of AI models that can understand and generate content in various languages.

(iv) Fine-grained Annotation for Specific Applications

Industries such as healthcare, legal, finance, and customer service are increasingly utilizing AI-driven solutions. Future trends will involve more fine-grained and specialized text annotation tailored to these specific domains, ensuring accurate and domain-specific language models.

(v) Emphasis on Bias Mitigation

Recognizing and mitigating biases within text data is crucial for fair and ethical AI. Future trends in text annotation will focus on identifying and mitigating biases in textual datasets to ensure AI models are fair and unbiased across various demographics and social contexts.

(vi) Semi-supervised and Active Learning Approaches

To optimize annotation efforts, future trends in text annotation might include the integration of semi-supervised and active learning techniques. These methods intelligently select the most informative samples for annotation, reducing the annotation workload while maintaining model performance.

(vii) Privacy-Centric Annotation Techniques

In alignment with broader data privacy concerns, text annotation will likely adopt techniques that ensure the anonymization and protection of sensitive information within text data, balancing the need for annotation with privacy preservation.

(viii) Enhanced Collaboration and Crowdsourcing Platforms

Similar to other data annotation domains, text annotation will benefit from collaborative and crowdsourced platforms that allow distributed teams to annotate text data efficiently. These platforms will offer improved coordination, quality control mechanisms, and scalability.

(ix) Continual Learning and Adaptation

As language evolves and new linguistic patterns emerge, text annotation will evolve towards continual learning paradigms. This will enable AI models to adapt and learn from ongoing annotations, ensuring they remain relevant and up-to-date.

(x) Explainable AI through Annotation

Text annotation may involve creating datasets that facilitate the development of explainable AI models. Annotations focused on explaining decisions made by AI systems can aid in building transparent and interpretable language models.

These future trends in text annotation are driven by the evolving nature of AI technology, the increasing demands for more accurate and specialized AI models, ethical considerations, and the need for scalable and efficient annotation processes.

The exploration of text annotation highlights its crucial role in AI's language understanding. This journey revealed:

(i) Text annotation is vital for AI to interpret human language nuances across industries like healthcare, finance, and more.

(ii) Challenges in annotation, like dealing with ambiguity and subjectivity, stress the need for ongoing innovation.

(iii) The best practices and guidelines for text annotation and various available text annotation tools.

(iv) The future promises advancements in language processing, bias mitigation, and contextual understanding.

Overall, text annotation is a cornerstone in AI's language comprehension, fostering innovation and laying the groundwork for seamless human-machine communication in the future.

Frequently Asked Questions

1. what is text annotation & why is it important.

Text annotation enriches raw text by labeling entities, sentiments, parts of speech , etc. This labeled data trains AI models for better language understanding. It's crucial for improving accuracy in tasks like sentiment analysis, named entity recognition, and more. Annotation aids in creating domain-specific AI models and standardizing data, facilitating precise human-AI interactions.

2. What are the different types of annotation techniques?

Annotation techniques involve labeling different aspects of text data for training AI models. Types include Entity Annotation (identifying entities), Sentiment Annotation (labeling emotions), Intent Annotation (categorizing purposes), Linguistic Annotation (marking grammar), Relation Extraction, Coreference Resolution, Temporal Annotation , and Speech Recognition Annotation .

These techniques are vital for training models in various natural language processing tasks, aiding accurate comprehension and response generation by AI systems.

3. What is in-text annotation?

In-text annotation involves adding labels directly within the text to highlight attributes like phrases, keywords, or sentences. These labels guide machine learning models. Quality in-text annotations are essential for building accurate models as they provide reliable training data for AI systems to understand and process language more effectively.

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Annotating Texts

What is annotation.

Annotation can be:

  • A systematic summary of the text that you create within the document
  • A key tool for close reading that helps you uncover patterns, notice important words, and identify main points
  • An active learning strategy that improves comprehension and retention of information

Why annotate?

  • Isolate and organize important material
  • Identify key concepts
  • Monitor your learning as you read
  • Make exam prep effective and streamlined
  • Can be more efficient than creating a separate set of reading notes

How do you annotate?

Summarize key points in your own words .

  • Use headers and words in bold to guide you
  • Look for main ideas, arguments, and points of evidence
  • Notice how the text organizes itself. Chronological order? Idea trees? Etc.

Circle key concepts and phrases

  • What words would it be helpful to look-up at the end?
  • What terms show up in lecture? When are different words used for similar concepts? Why?

Write brief comments and questions in the margins

  • Be as specific or broad as you would like—use these questions to activate your thinking about the content
  • See our handout on reading comprehension tips for some examples

Use abbreviations and symbols

  • Try ? when you have a question or something you need to explore further
  • Try ! When something is interesting, a connection, or otherwise worthy of note
  • Try * For anything that you might use as an example or evidence when you use this information.
  • Ask yourself what other system of symbols would make sense to you.

Highlight/underline

  • Highlight or underline, but mindfully. Check out our resource on strategic highlighting for tips on when and how to highlight.

Use comment and highlight features built into pdfs, online/digital textbooks, or other apps and browser add-ons

  • Are you using a pdf? Explore its highlight, edit, and comment functions to support your annotations
  • Some browsers have add-ons or extensions that allow you to annotate web pages or web-based documents
  • Does your digital or online textbook come with an annotation feature?
  • Can your digital text be imported into a note-taking tool like OneNote, EverNote, or Google Keep? If so, you might be able to annotate texts in those apps

What are the most important takeaways?

  • Annotation is about increasing your engagement with a text
  • Increased engagement, where you think about and process the material then expand on your learning, is how you achieve mastery in a subject
  • As you annotate a text, ask yourself: how would I explain this to a friend?
  • Put things in your own words and draw connections to what you know and wonder

The table below demonstrates this process using a geography textbook excerpt (Press 2004):

A chart featuring a passage from a text in the left column and then columns that illustrate annotations that include too much writing, not enough writing, and a good balance of writing.

A common concern about annotating texts: It takes time!

Yes, it can, but that time isn’t lost—it’s invested.

Spending the time to annotate on the front end does two important things:

  • It saves you time later when you’re studying. Your annotated notes will help speed up exam prep, because you can review critical concepts quickly and efficiently.
  • It increases the likelihood that you will retain the information after the course is completed. This is especially important when you are supplying the building blocks of your mind and future career.

One last tip: Try separating the reading and annotating processes! Quickly read through a section of the text first, then go back and annotate.

Works consulted:

Nist, S., & Holschuh, J. (2000). Active learning: strategies for college success. Boston: Allyn and Bacon. 202-218.

Simpson, M., & Nist, S. (1990). Textbook annotation: An effective and efficient study strategy for college students. Journal of Reading, 34: 122-129.

Press, F. (2004). Understanding earth (4th ed). New York: W.H. Freeman. 208-210.

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Text Annotation: The Complete Guide

Text Annotation

Viso Suite is the all-in-one solution for teams to build, deliver, scale computer vision applications.

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Data annotation allows machine learning algorithms to understand and interpret information. The annotations are labels that identify and classify data or associate different pieces of information with each other. AI algorithms use them as ground truths to adjust their weights accordingly. The labels are task-dependent and can be further categorized as an image or text annotation.

Text annotations associate meaning with textual information for ML algorithms to understand. They generate labels that allow ML algorithms to interpret the text in a human-like fashion. The process involves classifying blocks of text, tagging text elements for semantic annotation and understanding, or associating intent with conversational data. Each of these methodologies trains machine learning models for different practical use cases.

The article will discuss the following key points:

  • Definition and significance of text annotation
  • Text Annotation Methodologies
  • Text Annotation Use Cases

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What is Text Annotation?

The text annotation process aims to generate meaning from the text by highlighting key features such as parts of speech, semantic links, or general sentiment or intent of the document. Each annotation task labels text differently and is used for different use cases. A sentiment analysis application requires blocks of text to be classified into a sentiment category. Sentiment annotations are created as follows:

“The sky is blue” – Neutral

“I am very excited about the field trip to the museum” – Happy

“I should have scored higher on the math test. It’s not fair.” – Angry

However, not all text annotations are done as above. For example, in semantic understanding, we label each part of a sentence individually, such as the subject and object.

The text documents and their associated annotations (labels) are used to train ML models for text understanding. The model learns to associate the annotations with the provided input corpus and then replicates the same association with unseen data.

Challenges for Text Annotation

The annotation process is straightforward, but it carries certain challenges. The challenges hamper the annotation quality and impact model performance. These include:

  • Time-Consuming : Text corpora can be extensive, and manually labelling the entire dataset is time and resource-consuming. Certain AI-assisted annotation tools do speed up the process, but their performance varies due to the unstructured nature of the data, and human involvement is a necessity.
  • Mis-classified Intent : Sentiments and intents in text documents can be difficult to decipher. Real-world datasets are filled with ambiguities like sarcasm, making annotating the user’s intent or feelings difficult.
  • Text Variations : Text is a form of expression and can have the same meaning even with different structures or wording. A quality dataset must include all such variations and be annotated. Diversity increases the complexity of collected and annotated data.

Types of Text Annotation Methods

Text can be labelled using various methods, and each annotation method targets a different problem. Here are some of the most prominent text annotation methods used in the machine learning domain.

Text Classification

Text documents can be classified into different categories depending on the task at hand. The classification process associated each text document with a single label, and this association is later used to train ML algorithms. It can be further categorized as follows:

  • Sentiment Annotation : Texts like customer reviews and social media posts usually express different sentiments. Such text chunks can be classified into classes like ‘Happy,’ ‘Sad,’ ‘Angry,’ or ‘Excited.’ The annotations can be further simplified by reducing the classes to ‘Positive,’ ‘Negative,’ or ‘Neutral.’ Class granularity is decided based on the task requirements. Sentiment annotations train sentiment classifiers used in the retail business for product review analysis.

Sentiment Analysis

  • Topic Modelling : Text documents can also be classified according to the information they contain and the topic they represent. For example, educational texts can be classified into subjects like ‘Mathematics,’ ‘Physics,’ ‘Biology’ etc. These topics act as labels for the corpus and power topic modeling applications. Moreover, topic modeling annotations are also used in LLMs to help the chatbot understand the context.
  • Spam Annotation : Text collections from emails or messaging platforms can be annotated as ‘Spam’ or ‘Safe.’ These annotations are used to train spam classifiers for security applications.

Entity Tagging

Natural language text comprises various elements that give meaning to the text’s semantics. Entity tagging labels these elements into their respective classes. The type of entities tagged is dependent on the problem to be addressed. Understanding text semantics and its grammatical structure requires tagging parts of speed (POS), like nouns, verbs, and adjectives.

Other problems requiring generic understanding require tagging named entities like people and places and recognizing elements like addresses, contact numbers, etc.

Named Entity Recognition

An important distinction between classification and entity tagging is that the former assigns a single label to an entire document. In contrast, the latter assigns a label to every word in the document.

Entity Linking

Entity linkage is similar to entity tagging as it also identifies individual elements present within the text. However, it aims to link the present entity to an external knowledge base to create a wider context. For example, in the text, “Elon Musk is the founder of SpaceX”, entity linking would link ‘Elon Musk’ to the relevant information in the database to understand who the person is to better understand the text.

Intent Annotation

Chatbots recognize text commands based on the user’s intent and try to generate an appropriate response. Intent annotation classifies the text into intent categories such as request, question, command, etc. These allow chatbots to navigate the conversation and answer queries or execute actions.

Sequence-to-Sequence Annotation

Modern sequence-to-sequence models map a text sequence onto another. A popular example is text summarization models that accept a large text body as input and output a significantly compressed sequence. Another case is human language translation, where the output is a similar sequence to the input but in a different language.

Language Translation

In either application, the annotations are also sequences of text that link to the original text document. For example, for the sentence ‘The weather is nice’ , the annotation for a French translation model would be the following sequence ‘il fait beau’ .

Applications

The text annotation techniques discussed above power various Natural Language Processing (NLP) applications. The applications have several use cases in various domains. They enable the automation of time-consuming tasks and replace manual labor with computer-operated workflows. Let’s discuss some key use cases of text annotation.

Named Entity Recognition (NER)

NER is a popular NLP application that identifies entities present within the text. The entities can include names, locations, date, and time. These entities allow computers to analyze text and execute automated workflows. For example, NER models can recognize the location, date, and time mentioned in corporate emails and set automatic reminders for a meeting.

It is also used to extract useful entities from large bodies of text. Medical practitioners can use it to retrieve medicine and patient names from large medical files to understand what was prescribed to what patient.

Moreover, NER models also utilize context windows to understand the entity’s identity. For example, in the sentence ‘ Paris is a beautiful place ’, the corresponding text helps identify that ‘ Paris ’ is a location and not a person.

Customer Support Chatbots

Chatbots are quickly fulfilling the need for efficient customer dealing and support. Modern chatbots use a combination of classification, entity tagging, and intent identification to break down a customer query. The mentioned techniques help them understand the semantics and respond appropriately.

ChatBot Application

They can recognize entities from the text to understand which product or category a person is referring to. Furthermore, they can identify the user’s intent, whether they are inquiring about a product, requesting a refund, or registering a complaint. The intent classification helps the chatbot generate appropriate responses and execute required actions. Moreover, they also utilize sentiment analysis to recognize whether a customer is angry or upset and redirect the query to a human.

Customer Analysis

Customers often post product reviews on social media or via a designated portal from the company. Sentiment analysis allows businesses to segregate these reviews into positives and negatives without going through them manually. The negative reviews are further observed for any recurring patterns or products that require fixing. Sentiment analysis helps organizations improve product quality and customer satisfaction.

Article Segregation

Techniques like topic modelling and entity recognition segregate articles into different subjects. This is particularly prominent for news broadcasters, who segregate news articles into topics such as politics, social issues, global news, etc. The same techniques are used by social media platforms to categorize content into topics.

The categorized documents are further analyzed for hate speech or trending subjects. These analyses are used to develop new features to attract new users.

Text Annotation: Key Takeaways

Natural Language Processing (NLP) is an integral part of the AI ecosystem and has various applications powered numerous workflows. Behind these NLP models are the text annotations that add meaning to the text allow models to learn natural language patterns.

This article discussed text annotation in detail, covering the various techniques used and their use cases in the industry. Here’s what we learned:

  • Text annotations associate labels with blocks of text.
  • Annotating text is challenging due to the unstructured and ambiguous nature of the data.
  • Sentiment Classification
  • Topic Classification
  • Entity Recognition
  • Intent Classification
  • Entity Linkage
  • Classification annotations often associate a single label with an entire text document.
  • Entity-level annotations associate labels with individual words.

Text annotation powers various NLP use cases like sentiment analysis, chatbots, and document analysis. Here are some additional resources to catch up on the latest AI developments:

  • N-Shot Learning: Zero Shot vs. Single Shot vs. Two Shot vs. Few Shot
  • Llama 2: The Next Revolution in AI Language Models – Complete 2024 Guide
  • AI Can Now Create Ultra-Realistic Images and Art from Text (2024)
  • Looking into Natural Language Processing Tasks

Explore Image Annotation with Viso Suite

Modern computer vision algorithms require a vast amount of data for annotated projects. Viso Suite offers a image annotation platform that encourages efficiency and collaboration. The toolset offers semi-automatic annotation for creating high-quality datasets that is shared and reviewed across the team.

Viso.ai provides a no-code end-to-end platform for creating and deploying CV applications. We offer a vast library of vision-related models with applications across various industries. We also offer data management and annotation solutions for custom training. Book a demo to learn more about Viso suite.

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What is Annotating?

Annotating is any action that deliberately interacts with a text to enhance the reader's understanding of, recall of, and reaction to the text. Sometimes called "close reading," annotating usually involves highlighting or underlining key pieces of text and making notes in the margins of the text. This page will introduce you to several effective strategies for annotating a text that will help you get the most out of your reading.

Why Annotate?

By annotating a text, you will ensure that you understand what is happening in a text after you've read it. As you annotate, you should note the author's main points, shifts in the message or perspective of the text, key areas of focus, and your own thoughts as you read. However, annotating isn't just for people who feel challenged when reading academic texts. Even if you regularly understand and remember what you read, annotating will help you summarize a text, highlight important pieces of information, and ultimately prepare yourself for discussion and writing prompts that your instructor may give you. Annotating means you are doing the hard work while you read, allowing you to reference your previous work and have a clear jumping-off point for future work.

1. Survey : This is your first time through the reading

You can annotate by hand or by using document software. You can also annotate on post-its if you have a text you do not want to mark up. As you annotate, use these strategies to make the most of your efforts:

  • Include a key or legend on your paper that indicates what each marking is for, and use a different marking for each type of information. Example: Underline for key points, highlight for vocabulary, and circle for transition points.
  • If you use highlighters, consider using different colors for different types of reactions to the text. Example: Yellow for definitions, orange for questions, and blue for disagreement/confusion.
  • Dedicate different tasks to each margin: Use one margin to make an outline of the text (thesis statement, description, definition #1, counter argument, etc.) and summarize main ideas, and use the other margin to note your thoughts, questions, and reactions to the text.

Lastly, as you annotate, make sure you are including descriptions of the text as well as your own reactions to the text. This will allow you to skim your notations at a later date to locate key information and quotations, and to recall your thought processes more easily and quickly.

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  • Last Updated: Apr 25, 2024 2:50 PM
  • URL: https://research.ewu.edu/writers_c_read_study_strategies

A Step-by-Step Guide to Text Annotation [+Free OCR Tool]

Deval Shah

AI systems use huge amounts of annotated data to train highly accurate and target-specific models. During the annotation process, a metadata tag is used to define the characteristics of a dataset. 

In-text annotation, that metadata includes tags that highlight attributes such as phrases, keywords, or sentences. The quality of text annotations is crucial to building high precision models. This article will focus on different aspects of text annotation and its use cases.

Here’s what we’ll cover:

What is Text Annotation

Types of text annotation, text annotation use cases.

And in case you are here searching for a kick-ass text annotation tool—we got you covered!

You can easily annotate text data on V7 and train your own model, or... use a public V7 Text Scanner model to detect and read text in your images in any language and alphabet, including handwritten text.

Looks cool? Try it out!

Connect multiple AI models and LLMs to solve any back office process

Here's more info:

  • V7 Image Annotation
  • V7 Video Annotation
  • V7 Labeling Services
  • V7 Model Training
  • V7 Dataset Management

Don't forget to also check out our Open Datasets repository to find quality data for training your models!

Now, let's dive in!

Text Annotation for machine learning consists of associating labels to digital text files and their content. Text annotation converts a text into a dataset that can be used to train machine learning and deep learning models for a variety of Natural Language Processing and Computer Vision applications. 

In simple terminology, Text Annotation is appending notes to the text with different criteria based on the requirement and the use case. Annotation can be annotating words, phrases, sentences, etc., and assigning a label to them like proper names, sentiment, intention, etc.

annotated text set

Text Recognition vs. Document Processing

Text Recognition is the process of converting printed and handwritten texts into machine-readable text. We refer to it as Optical Character Recognition (OCR), which recognizes the texts from any document in pdf, doc, or image in jpg, png, jpeg, or similar.

Document processing , which is also known as IDP (Intelligent Document Processing) not only recognizes the text but also understands the semantics of it. IDP leverages text recognition and understands the meaning of the recognized text using text annotation. Those kind of models require annotated data.

Text Recognition and Document Processing are different concepts where Text Recognition can be thought of as the subtask in Document Processing.

Now, let's discuss different types of text annotation!

Text Annotation is categorized into multiple types based on what part of the text is annotated and what that portion of text signifies.

Sentiment Annotation

Sentiment Annotation is the annotation of the sentences with the corresponding sentiment of the sentence. It is difficult to determine the emotion of the sentences over a text or handwritten message, but it's not impossible. For sentiment analysis, we require annotated data using sentiment annotation pictured below.

Sentiment Text Annotation on three sentences

As you can see, the sentences have the corresponding sentiments attached tothem. However, these are pretty much clear sentences without ambiguity. But in the case of complex sentences, precise sentiment annotation is required, especially for the use cases that are not generalized and have particular sentiment for a specific kind of text.

E-Commerce applications such as Flipkart or Amazon use this kind of annotation to understand the customer's feedback from their comments about the products. Likewise, sentiment annotation is leveraged for preparing the dataset for training sentiment analysis models that categorize the texts into various labels such as happy, sad, angry, positive, negative, neutral, etc.

Intent Annotation

Intent Annotation annotates the sentences to detect the intent that matches the correct context of the sentences. This kind of annotation technique is widely used in virtual assistants and chatbots.

In these cases, the response is given based on the intent detected from the previous message received by the end-user.

A chat with a chatbot demonstrating intent annotation

As shown above, when the user replies, the intent of the message is detected, and processing that message, the chatbot then delivers the response.

The responses are designed in such a way that a particular set of answers will be delivered when a specific intent is triggered. In the example, the "Hello" message by the user detected "Greetings Intent" and the designed Welcome message is sent for the response. The subsequent reply from the user detects "Recharge Complaint Intent". Likewise, the cycle continues, and the chatbot replies with appropriately designed messages for particular intents. 

Here, the need arises for intent annotation to train the assistants to detect correct intents with high precision because it might be annoying for the user if the chatbot is unable to reply with the right message.

Siri, Alexa, and Cortana are well-known virtual assistants that show promising performance concerning accuracy. These assistants are intelligent as they are trained on large amounts of intent annotated data . For example, “Hi”, “Hello”, “Hola”, “Hey”, etc. detects the intent greeting and the response will be based on this intent which will revert to something like “Hello, how can I help you?” These categorize into intents like to request, command, assertion, negation, etc., or more specific to the use case "Recharge Complaint Intent" as in the above example.

Entity Annotation

Entity Annotation annotates the key phrases, named entities, or parts of speech of the sentences. Entity annotation helps drive attention to the crucial details of the long text. This annotation also helps prepare the dataset for models that extract different kinds of entities from a huge amount of text. It is widely used in most NLP-related tasks.

Entity recognition is a natural language processing technique that can automatically scan entire articles and pull out some fundamental entities in a text and classify them into predefined categories.

For example, given the sentence "Paris is the capital of France" , the idea is to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred to as "Paris"

Example showing annotation of different entities

As shown in the image, entities such as DATE, EVENT, GPE (Geo-Political Entity), etc. is annotated. This enables machines to understand the text in a much better way.

The entities can be any of the following:

1. Keyword phrase: Example- healing is difficult, the decision from the mayor, etc.

2. Parts of speech: Adjectives, Nouns, Verbs, etc. Example- reading, dead, suspicious, etc.

3. Named Entity: Location, person name, organization name, date, event, etc.

For example, we can extract detailed numerical information from medical reports or extract entities such as organization, person name, location, law sections, etc. from the legal documents. Moreover, a document like a thesis or a research paper is difficult to read due to the micro-level details and the volume of the text.

In these types of cases, entity annotation helps read the necessary information at a glance with ease.

Text Classification

Text Classification as the name suggests categorizes the documents or the group of sentences under a particular label. This annotation helps segregate a large volume of texts or documents into the appropriate categories such as document classification, product categorization, and sentiment annotation.

Classification of different contextual tweets into respective labels

Above given are some tweets which are classified in some particular labels shown like Education, politics, and entertainment. The contents are annotated in the same way to prepare a dataset for the machine learning classification models.

💡 Pro Tip: Check out Image Classification Explained: An Introduction [+V7 Tutorial].

In E-Commerce, the products are categorized based on the content and the details of the product. Likewise, the tweets on Twitter, the news are categorized based on the text and we can have different categories such as education, research, politics, entertainment, etc.

Linguistic Annotation

Linguistic Annotation refers to the annotation of the language-related details of the text or speech such as semantics, and phonetics. This annotation helps understand the phonetics and the discourse of the content. In addition, this also includes identifying the intonation, stress, pauses, etc.  

Linguistic Annotation can be further divided into the following three categories of annotation:

  • Discourse Annotation: The linking of anaphors, and cataphors to their antecedent or descendent subjects. 

Example: Mike is kind to his colleagues. He often helps them with their queries.

Here, Mike is referred to by "He" and his colleagues are referred to by "them". A human understands this reference but the machine requires annotation to learn this kind of linking of the sentences.

  • Semantic Annotation: The labeling of the metadata of the original text. 

Example: OTT Platforms are trendy.

Here, OTT is the jargon that can be annotated with the full form "Over The Top" and the examples like "Netflix", "Amazon Prime Videos", etc.

  • Phonetic Annotation: The annotation of the stress, tone, and pauses. 

Chatbots, virtual assistants, etc. leverages linguistic annotation to understand the linguistic details of the replies from end-user to respond to them with better clarity.

The annotation techniques are cohesive—sentiment and intent annotation can be viewed as the sub-type of text classification and linguistics annotation can be considered similar to entity annotation.

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How to annotate text on V7 [Tutorial]

One can annotate text on V7 if needed but V7’s public Text Scanner model might be a better choice because it saves time and it is highly accurate to pseudo annotate labels.

V7 has added a public Text Scanner model to its Neural Networks page to help you detect and read the text in your images automatically. In this tutorial, we'll take a quick video tour of how to use the feature, followed by step-by-step instructions for setting up your own text scanner.

💡 Pro Tip: Read The Essential Guide to Neural Network Architectures.

Before we can start effortlessly pulling text from images and documents, we'll need to get three quick setup steps out of the way:

  • Turn on the public Text Scanner model on the Neural Networks page.
  • Create a new bounding box or polygon class that contains the Text subtype in the Classes page of your dataset. You can optionally add an Attributes subtype to distinguish between different types of text.
  • Add a model stage to your workflow under the Settings page of your dataset. Select the Text Scanner model from the dropdown list, and map your new text class. If the model stage is the first stage in your workflow, select Auto-Start to send all new images through the model automatically.

That's it! Now you're ready to sit back and let the model detect and read text automatically instead of you making efforts to annotate manually. 

V7 text scanner will detect and read text on any kind of image, whether it's a document, photo, or video. As it is extensively pre-trained, it will be able to read characters that humans may find difficult to interpret.

White car brand and license plate recognition using V7 Text Scanner

💡 Pro tip: Looking for the perfect OCR dataset? Check out 20+ Open Source Computer Vision Datasets.

Finally, here are some of the most prominent applications of text annotation.

Text data Annotation plays a vital role in the healthcare domain , especially today when we deal with AI-based services in the medical field such as patients records management, online medical consultancy healthcare chatbots, etc.

For a domain like healthcare, we can not take any risks regarding data accuracy, as it is concerned with the patient’s life, and, therefore, a large amount of quality annotated data is required.

Here are some of the healthcare use cases where text annotation plays an important role:

  • Entity Annotation for extracting details in the medical reports such as the numerical data (example: blood pressure level, hemoglobin, etc.) or some useful keyphrases
  • Entity Annotation for annotating medicines, dose, time for taking medicine, etc. from the prescription given by the doctor.
  • Intent Annotation and linguistics annotation for research and study purpose which annotates details and crux of the context making it easier to go through the large volume of the content.
  • Sentiment Annotation for the feedback purpose in any hospital, laboratory, or healthcare applications.
  • Intent Annotation, Linguistics Annotation, and Semantic Annotation for the customer service in the healthcare applications as well as the chatbots.

💡 Pro tip: Check out 21+ Best Healthcare Datasets for Computer Vision and learn How to Annotate Medical Images.

Banking also has quite an extensive range of use cases as nowadays, we use online banking that includes interacting with the applications and websites for transactions and other services given by the bank

Some use cases for data labeling in banking include:

  • Text Classification helps customer churn prediction.
  • Intent, sentiment, and linguistic annotation are used for customer services and chatbots.
  • Entity Annotation is utilized for extracting entities such as name, amount, bank account no., IFSC code, etc. from various types of forms.

The logistics and Supply Chain industry is expanding at an astonishing rate and so is the use of technology in it. From billing and invoice labeling to virtual assistants, there is a surplus data generated every single day.

Customer Care Virtual Assistant detect intent by identifying a particular entity from the user message.

When the customer approaches for a rate inquiry, the virtual assistant asks a few questions and immediately provides the approximate rate. The entities and the useful information are extracted from the responses, processed further and the rates are provided. 

Data Annotation in logistics is also used as follows:

  • Entity annotation for annotation of the names, amounts, order no, items, etc. from the bills and invoices.
  • Sentiment and Entity Annotation for the customer feedback.

💡 Pro tip: Speed up your labeling 10x by using V7 auto annotation tool.

The use of annotation in the government sector is a little bit similar to banking but has a broad spectrum than banks. The government sector includes the education department, research, food and drugs, legal, income tax department, forensics, etc.

The use of annotation in this domain encapsulates:

  • The intent, entity, and linguistic annotation for all the above-discussed sector’s customer service, chatbots, and virtual assistants.
  • Text classification for categorizing legal cases in criminal, civil, etc. based on the content of the cases.
  • Linguistic Annotation for police and crime branch for detecting tones, semantics, etc. of the criminal and various cases and reports.
  • Entity Annotation for all the government documents annotating the entities such as names, department, location, and key phrases.

Media and News

Media and News is another sector having a lot of textual content where the Annotation can be widely used to understand the content.

Data Annotation in media and news are in the following use cases:

  • Entity annotation for annotation of various entities such as names, location, key phrases, numbers, etc. from various articles.
  • Text Classification for categorizing the content into various labels for news such as sports, education, government, domestic, international, entertainment, etc.
  • Linguistic Annotation and Semantic Annotation for annotation of the phonetics, semantics, and discourse for the articles and news reports.

Apart from the use cases mentioned above, there are various other subdomains such as Research, Education, Entertainment, E-Commerce, Multimedia, etc.

Text Annotation: Key takeaways

Text Annotation plays an important role today as we want a large amount of data for training various Machine Learning and Deep Learning models.

Well annotated data improves the quality of data that further enhances the accuracy of the AI models. So, for an AI model to attain higher accuracy and precision, the first step of the pipeline is to prepare well-annotated data, which demands the use of Text Annotation in the case of Natural Language Processing.

annotated text set

Deval is a senior software engineer at Eagle Eye Networks and a computer vision enthusiast. He writes about complex topics related to machine learning and deep learning.

“Collecting user feedback and using human-in-the-loop methods for quality control are crucial for improving Al models over time and ensuring their reliability and safety. Capturing data on the inputs, outputs, user actions, and corrections can help filter and refine the dataset for fine-tuning and developing secure ML solutions.”

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The ultimate guide to text annotation.

20 December 2021

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Machine learning and artificial intelligence are tools in the modern technology toolbox, but they do not receive a lot of notoriety. So, you might be surprised to learn that over 70% of companies use text as their primary data for AI solutions—this, according to the 2020 State of AI and Machine learning report. There are many media types in the digital platform, such as text, audio, images, and video. Text is a common form of media preferred to communicate for personal and business purposes. Over the years, organizations have accumulated text data in an unstructured format. How can we use this text to our advantage?

Text annotation is the science—or perhaps the art—of adding information or metadata to define the characteristics of sentences, for example semantics or sentiments. It helps the machine distinguish or recognize words in a sentence, making it more sensible. This annotated text serves as a training dataset for AI and ML applications. 

Why do we need to annotate text?

A precise training dataset gives the AI model the ability to learn and grow to interpret human language more consistently. Providing a complete set of training data at an early stage to machine learning algorithms can help develop self-predicting AI. In many cases, AI and ML developers prefer human annotators to highlight texts for various dialects, sentiments, meaning, and usage to maintain and improve accuracy.

Once the AI model starts learning the nuances of human language, it can label the keywords, phrases, or sentences. The main goal of text annotation is to assist the engine to understand human language.     

Types of Text Annotation Techniques

Entity Annotation

Entity annotation teaches the AI model to recognize parts of speech, named entities, and key phrases in the text. Entity annotation is a vital task for Natural Language Understanding (NLU) workflow. Natural language understanding is a part of artificial intelligence that uses computer software to recognize the inputs in the form of sentences.

A single sentence has much metadata and information that can offer insightful information. For example, consider the phrase, “Mark lives in Ohio.” Mark is the Name, and Ohio is the Place. Once an AI model is presented with datasets of Name and Place, the AI model can actively label names and places in subsequent texts.

Some types of entity annotation are:

  •  Named entity recognition (NER) is the annotation of entities with proper names.
  • Keyphrase tagging is the process of labeling keywords or keyphrases in text data.
  • Part-of-speech (POS) tagging annotates functional elements of speech such as adjectives, nouns, adverbs, verbs, etc.

Entity linking

Entity linking is the process of annotating entities within the given text. It is often used to improve the user experience for search-related functions and involves the process of annotating entities to large repositories of data.

For example, “Paris is a beautiful city.” However, here Paris refers to a city and not a person’s name. Linking Paris to a more extensive database like Wikipedia gives information about the city.

Types of entity linking are:

  • End-to-end entity linking is the combination of analyzing and annotating entities, followed by entity disambiguation.
  • Entity disambiguation links named entities to a broader knowledge database such as Wikipedia.

Text classification

Text classification annotates an entire line of text or content with a single label. The annotator reads the text, analyzes it based on the content, intent, or sentiment, and categorizes it based on the requirements of a predetermined tag.

A few common methods to train AI- and ML-enabled applications are:

  • Document classification is used to sort and recall text-based content.
  • Product categorization in e-commerce sites categorize products and improve the search experience.
  •  Sentiment annotation labels text based on emotions and sentiments.

Sentiment annotation

One of the challenging tasks for AI and ML is to identify the human emotions in the text which are notoriously complicated to understand. Expecting a machine to understand these is unrealistic. Instead, machines are fed with sentiment-annotated text data to help them predict human emotions.

Another technique for adding sentiment involves the annotation of customer reviews. When a review is labeled as either positive, negative, or neutral, that helps the AI system to further learn about sentiment.

Consider the following examples of customer reviews:

The treadmill is good for small spaces. (Positive)

The quality of the toy is poor. (Negative)

Linguistic annotation

Linguistic annotation is when you identify and label language data in text or audio, for example, grammatical, semantic, phonetic elements, and audio data. These annotated datasets are commonly used in chatbots, virtual assistants, search engines, machine translation, and more. Types of linguistic annotation include:

  • Discourse annotation: Link the anaphors and cataphors to their antecedent subjects.

For example, Jessica had to work during the holidays. She felt sad about it.

  • Part-of-speech (POS) tagging: label different function words within the text.
  • Phonetic annotation: label intonation, stress, and natural pauses in speech.
  • Semantic annotation: label word definitions.

Many organizations prefer human annotators to fulfill text annotation. The training datasets from the human annotators are generally considered to be more accurate and unbiased, making the AI model intelligent and enables further learning.

At PreludeSys, we offer impeccable text annotation service with access to cutting-edge technology and expertise. Our dedicated team is trained to provide customized text annotation as per your business and project requirements. We understand the struggle of handling unstructured texts which is why we devise a strategic text annotation plan that is highly efficient and cost-effective for your organization.

If you want to learn more about text annotation and other data annotation services, get in touch with us today . Help your AI achieve cognitive knowledge by feeding it accurately annotated text data.

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What is Text Annotation?

What are some types of annotation styles, how is text annotated, text annotation: key takeaways, encord blog, a complete guide to text annotation.

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Nikolaj Buhl

Have you ever considered the sources from which AI models acquire language? Or the extensive effort required to curate high-quality data to power today's sophisticated language systems? 

By the end of the guide, you will be able to answer the following questions:

  • What is text annotation?
  • What are some types of text annotation? 
  • How is text annotation?

Traditionally, text annotation involves adding comments, notes, or footnotes to a body of text. This practice is commonly seen when editors review a  draft, adding  notes or useful comments (i.e. annotations) before passing it on for corrections.

In the context of machine learning , the term takes on a slightly different meaning. It refers to the systematic process of labeling pieces of text to generate a ground-truth . The labeled data ensures that a supervised machine learning algorithm can accurately interpret and understand the data.

What Does it Mean to Annotate Text?

In the data science world, annotating text is a process that requires a deep  understanding of both the problem at hand and the data itself to identify relevant features and label them so. This can be likened to the task of labeling cats and dogs in several images for image classification . 

In text classification, annotating text would mean looking at sentences and marking them, putting each in predefined categories; like labeling online reviews as positive or negative, or news clippings as fake or real.

More tasks, such as labeling parts of speech (like nouns, verbs, subjects, etc.), labeling key phrases or words in a text for named entity recognition (ner) or to summarize a long article or research paper in a few hundred words all come under annotating text.

A Comprehensive Guide to Named Entity Recognition (NER

A Comprehensive Guide to Named Entity Recognition (NER) (Turing.com)  

What are the Benefits of Text Annotation?

Doing what we described above enables a machine learning algorithm to identify different categories and use the data corresponding to these labels to learn what the data from each category typically looks like. This speeds up the learning task and improves the algorithm’s performance in the real world.

Learning without labels, while common today in NLP, is challenging as it is left to the algorithm to identify the nuances of the English language without any additional help and also recognize them when the model is put out in the real world. In text classification, for instance, a negative piece of text might be veiled in sarcasm—something that a human reader would instantly recognize, but an algorithm might just see the sarcastically positive words as just positive! Text annotations and labels are invaluable in these cases.

Large companies that are developing powerful language models today also, on the other hand, rely on text annotation for a number of important use cases. For social media companies, that includes flagging inappropriate comments or posts, online forums to flag bots and spammy content, or news websites to remove fake or low-quality pieces. Even apps for basic search engines and chatbots can be trained to extract information from their queries.

Intent and Entity - Example

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Since there are several tasks of varying nature for language interpretation in natural language processing, annotating and preparing the training data for each of them has a different objective. However, there are some standard approaches that cover the basic NLP tasks like classifying text and parts of text. While these may not cover generative text tasks like text summarization, they are important in understanding the different approaches to label a text.

Text Classification

Just as it sounds, a text classification model is meant to take a piece of text (sentence, phrase or paragraph) and determine what category it belongs to. Document classification involves the categorization of long texts, often with multiple pages. This annotation process involves the annotators reading every text sample and determining which one of the context-dependent predefined categories each sample belongs to.

Typical examples are binning news clippings into various topics, sorting documents based on their contents, or as simple as looking at movie plot summaries and mapping them to a genre (as shown in some examples below).

Genre Classification Dataset IMDb taken from Kaggle

Genre Classification Dataset IMDb | Kaggle

Sentiment Annotation

Similar to text classification in process and strategy, the annotator plays a larger role in labeling a dataset for sentiment-related tasks. This task requires the annotator to interpret the text and look for the emotion and implicit context behind it—something that is not readily apparent to humans or machines when looking at the text.

Typical examples include sentiment analysis of a subject from social media data, analyzing customer feedback or product reviews, or gauging the shift in public opinion over a period of time by tracking historical texts.

Entity Annotation

Often understanding natural language extends to recalling or extracting important information from a given text, such as names, various numbers, topics of interest, etc. Annotating such information (in the form of words or phrases) is called entity annotation.

Annotators look for terms in a text of interest and classify them into predefined categories such as dates, countries, topics, names, addresses, zip codes, etc. A user can look up or extract only the pertinent information from large documents by using models trained on such a dataset to quickly label portions of the text. Semantic annotation involves a similar process, but the tags are often concepts and topics.

Keyphrase tagging (looking for topic-dependent keywords), NER (or named entity recognition) (covering a more extensive set of entities), and parts of speech tagging (understanding grammatical structure) come under entity annotation.

Intent Annotation

Another approach to annotating text is to direct the interpretation of a sentence towards an action. Typically used for chatbots, intent annotation helps create datasets that can train machine learning models to determine what the writer of the text wants. In the context of a virtual assistant, a message might be a greeting, an inquiry for information, or an actionable request. A model trained on a dataset where the text is labeled using intent annotation can classify each incoming message into a fixed category and simplify the conversation ahead.

Linguistic Annotation

This kind of text annotation focuses on how humans engage with the language—in pronunciation, phonetic sound, parts of speech, word meanings, and structure. Some of these are important in building a text-to-speech converter that creates human-sounding voices with different accents.

FLORS - Part-of-Speech Tagger

FLORS - Part-of-Speech Tagger

Now that we have established the various perspectives from which an annotator can look at their task, we can look at what a standard process of text annotation would be and how to annotate text for a machine learning problem. There is no all-encompassing playbook, but a well-defined workflow to go through the process step-by-step and a clear annotation guideline helps a ton.

What are Annotation Guidelines?

Text annotation guidelines are a set of rules and suggestions that act as a reference guide for annotators. An annotator must look at it and be able to understand the modeling objective and the purpose the labels would serve to that end. Since these guidelines dictate what is required of the final annotations, they must be set by the team familiar with the data and will use the annotations. 

These guidelines can begin with one of the annotation techniques, or something customized that defines the problem and what to look for in the data. They must also define various cases, common and potentially ambiguous, the annotator might face in the data and actions to perform for each such problem. 

For that purpose, they must also cover common examples found in the data and guidelines to deal with outliers, out-of-distribution samples, or other cases that might induce ambiguity while annotating. You can create an annotation workflow by beginning with a skeleton process, as shown below.

Curate Annotation Guidelines

Selecting a labeling tool, defining an annotation process, review and quality control.

  • First, define the modeling problem (classification, generation, clustering, etc.) that the team is trying to tackle with the data and the expected outcome of the annotation process like the fixed set of labels/categories , data format, and exporting instructions.
  • This can be extended to curating the actual guidelines that are comprehensive yet easy to revisit.
  • Getting the right text annotation tools can make all the difference between a laborious and menial task and a long but efficient process.
  • Given the prevalence of text modeling, there are several open-source labeling tools available. 

Below is an illustration of Doccano that shows how straightforward annotating intent detection and NER is!

annotated text set

Open Source Annotation Tool for Machine Learning Practitioners

  • Once the logistics are in place, it is important to have a reproducible and error-free workflow that can accommodate multiple annotators and a uniform collection of labeled samples.
  • Defining an annotation process includes organizing the data source and labeled data, defining the usage of the guidelines and the annotation tool, a step-by-step guide to performing the actual text annotation, the format of saving and exporting the annotations, and the review every labeled sample.
  • Given the commonly large sizes of text data teams usually work with, ensuring a streamlined flow of incoming samples and outgoing labels and reviewing each sample (which might get challenging as one sample can be as big as a multi-page document) is essential.
  • Along with on-the-fly review, have a collective look at the labeled data periodically to avoid generic label errors or any bias in labeling that might have come in over time.
  • It is also common to have multiple annotators label the same sample for consistency and to avoid any bias in interpretation, especially in cases where sentiment or contextual interpretation is crucial.
  • To check for the bias and reliability of multiple human annotators, there are statistical measures that can be used to highlight undesirable trends. Comparison metrics such as Cohen’s kappa statistic measure how often two annotators agree with each other on the same set of samples, given the likelihood they would agree by chance. An example of interpreting Cohen’s kappa is shown below. Monitoring such metrics would flag disagreement and expose potential caveats in understanding the data and the problem.

Cohen’s kappa statistic measure how often two annotators agree with each other on the same set of samples

Understanding Interobserver Agreement: The Kappa Statistic

This article underlines the roles text annotation plays for natural language processing use cases and details how you can get started with data annotation for text. You saw how:

  • high-quality data can significantly impact the training process for a machine learning model.
  • different tasks require different approaches and perspectives to annotating a text corpus; some require understanding the meaning of the text, while others require grammar and structure.
  • guidelines and choosing the right text annotation tool can simplify large-scale data annotation and improve reliability.
  • using strategies such as multiple annotators, quality metrics , and more can help generate high-quality labels.

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Visualizations in Databricks

With data becoming a pillar stone of a company’s growth strategy, the market for visualization tools is growing rapidly, with a projected compound annual growth rate (CAGR) of 10.07% between 2023 and 2028. The primary driver of these trends is the need for data-driven decision-making, which involves understanding complex data patterns and extracting actionable insights to improve operational efficiency.  PowerBI and Tableau are traditional tools with interactive workspaces for creating intuitive dashboards and exploring large datasets. However, other platforms are emerging to address the ever-changing nature of the modern data ecosystem. In this article, we will discuss the visualizations offered by Databricks - a modern enterprise-scale platform for building data, analytics, and artificial intelligence (AI) solutions. Databricks Databricks is an end-to-end data management and model development solution built on Apache Spark. It lets you create and deploy the latest generative AI (Gen AI) and large language models (LLMs). The platform uses a proprietary Mosaic AI framework to streamline the model development process. It provides tools to fine-tune LLMs seamlessly through enterprise data and offers a unified service for experimentation through foundation models. In addition, it features Databricks SQL, a state-of-the-art lakehouse for cost-effective data storage and retrieval. It lets you centrally store all your data assets in an open format, Delta Lake, for effective governance and discoverability. Further, Databricks SQL has built-in support for data visualization, which lets you extract insights from datasets directly from query results in the SQL editor. Users also benefit from the visualization tools featured in Databricks Notebooks, which help you build interactive charts by using the Plotly library in Python. Through these visualizations, Databricks offers robust data analysis for monitoring data assets critical to your AI models. So, let’s discuss in more detail the types of chart visualizations, graphs, diagrams, and maps available on Databricks to help you choose the most suitable visualization type for your use case. Effective visualization can help with effortless data curation. Learn more about how you can use data curation for computer vision Visualizations in Databricks As mentioned earlier, Databricks provides visualizations through Databricks SQL and Databricks Notebooks. The platform lets you run multiple SQL queries to perform relevant aggregations and apply filters to visualize datasets according to your needs. Databricks also allows you to configure settings related to the X and Y axes, legends, missing values, colors, and labels. Users can also download visualizations in PNG format for documentation purposes. The following sections provide an overview of the various visualization types available in these two frameworks, helping you select the most suitable option for your project. Bar Chart Bar charts are helpful when you want to compare the frequency of occurrence of different categories in your dataset. For instance, you can draw a bar chart to compare the frequency of various age groups, genders, ethnicities, etc. Additionally, bar charts can be used to view the sum of the prices of all orders placed in a particular month and group them by priority. Bar chart The result will show the months on the X-axis and the sum of all the orders categorized by priority on the Y-axis. Line Line charts connect different data points through straight lines. They are helpful when users want to analyze trends over some time. The charts usually show time on the X-axis and some metrics whose trajectory you want to explore on the Y-axis. Line chart For instance, you can view changes in the average price of orders over the years grouped by priority. The trends can help you predict the most likely future values, which can help you with financial projections and budget planning. Pie Chart Pie charts display the proportion of different categories in a dataset. They divide a circle into multiple segments, each showing the proportion of a particular category, with the segment size proportional to the category’s percentage of the total. Pie chart For instance, you can visualize the proportion of orders for each priority. The visualization is helpful when you want a quick overview of data distribution across different segments. It can help you analyze demographic patterns, market share of other products, budget allocation, etc. Scatter Plot A scatter plot displays each data point as a dot representing a relationship between two variables. Users can also control the color of each dot to reflect the relationship across different groups. Scatter Plot For instance, you can plot the relationship between quantity and price for different color-coded item categories. The visualization helps in understanding the correlation between two variables. However, users must interpret the relationship cautiously, as correlation does not always imply causation. Deeper statistical analysis is necessary to uncover causal factors. Area Charts Area charts combine line and bar charts by displaying lines and filling the area underneath with colors representing particular categories. They show how the contribution of a specific category changes relative to others over time. Area Charts For instance, you can visualize which type of order priority contributed the most to revenue by plotting the total price of different order priorities across time. The visualization helps you analyze the composition of a specific metric and how that composition varies over time. It is particularly beneficial in analyzing sales growth patterns for different products, as you can see which product contributed the most to growth across time. Box Chart Box charts concisely represent data distributions of numerical values for different categories. They show the distribution’s median, skewness, interquartile, and value ranges. Box Chart For instance, the box can display the median price value through a line inside the box and the interquartile range through the top and bottom box enclosures. The extended lines represent minimum and maximum price values to compute the price range. The chart helps determine the differences in distribution across multiple categories and lets you detect outliers. You can also see the variability in values across different categories and examine which category was the most stable. Bubble Chart Bubble charts enhance scatter plots by allowing you to visualize the relationship of three variables in a two-dimensional grid. The bubble position represents how the variable on the X-axis relates to the variable on the Y-axis. The bubble size represents the magnitude of a third variable, showing how it changes as the values of the first two variables change. Bubble chart The visualization is helpful for multi-dimensional datasets and provides greater insight when analyzing demographic data. However, like scatter plots, users must not mistake correlation for causation. Combo Chart Combo charts combine line and bar charts to represent key trends in continuous and categorical variables. The categorical variable is on the X-axis, while the continuous variable is on the Y-axis. Combo Chart For instance, you can analyze how the average price varies with the average quantity according to shipping date. The visualization helps summarize complex information involving relationships between three variables on a two-dimensional graph. However, unambiguous interpretation requires careful configuration of labels, colors, and legends. Heatmap Chart Heatmap charts represent data in a matrix format, with each cell having a different color according to the numerical value of a specific variable. The colors change according to the value intensity, with lower values typically having darker and higher values having lighter colors. Heatmap chart For instance, you can visualize how the average price varies according to order priority and order status. Heatmaps are particularly useful in analyzing correlation intensity between two variables. They also help detect outliers by representing unusual values through separate colors. However, interpreting the chart requires proper scaling to ensure colors do not misrepresent intensities. Histogram Histograms display the frequency of particular value ranges to show data distribution patterns. The X-axis contains the value ranges organized as bins, and the Y-axis shows the frequency of each bin. Histogram For instance, you can visualize the frequency of different price ranges to understand price distribution for your orders. The visualization lets you analyze data spread and skewness. It is beneficial in deeper statistical analysis, where you want to derive probabilities and build predictive models. Pivot Tables Pivot tables can help you manipulate tabular displays through drag-and-drop options by changing aggregation records. The option is an alternative to SQL filters for viewing aggregate values according to different conditions. Pivot Tables For instance, you can group total orders by shipping mode and order category. The visualization helps prepare ad-hoc reports and provides important summary information for decision-making. Interactive pivot tables also let users try different arrangements to reveal new insights. Choropleth Map Visualization Choropleth map visualization represents color-coded aggregations categorized according to different geographic locations. Regions with higher value intensities have darker colors, while those with lower intensities have lighter shades. Choropleth map visualization For instance, you can visualize the total revenue coming from different countries. This visualization helps determine global presence and highlight disparities across borders. The insights will allow you to develop marketing strategies tailored to regional tastes and behavior. Funnel Visualization Funnel visualization depicts data aggregations categorized according to specific steps in a pipeline. It represents each step from top to bottom with a bar and the associated value as a label overlay on each bar. It also displays cumulative percentage values showing the proportion of the aggregated value resulting from each stage. Funnel Visualization For instance, you can determine the incoming revenue streams at each stage of the ordering process. This visualization is particularly helpful in analyzing marketing pipelines for e-commerce sites. The tool shows the proportion of customers who view a product ad, click on it, add it to the cart, and proceed to check out. Cohort Analysis Cohort analysis offers an intuitive visualization to track the trajectory of a particular metric across different categories or cohorts. Cohort Analysis For instance, you can analyze the number of active users on an app that signed up in different months of the year. The rows will depict the months, and the columns will represent the proportion of active users in a particular cohort as they move along each month. The visualization helps in retention analysis as you can determine the proportion of retained customers across the user lifecycle. Counter Display Databricks allows you to configure a counter display that explicitly shows how the current value of a particular metric compares with the metric’s target value. Counter display For instance, you can check how the average total revenue compares against the target value. In Databricks, the first row represents the current value, and the second is the target. The visualization helps give a quick snapshot of trending performance and allows you to quantify goals for better strategizing. Sankey Diagrams Sankey diagrams show how data flows between different entities or categories. It represents flows through connected links representing the direction, with entities displayed as nodes on either side of a two-dimensional grid. The width of the connected links represents the magnitude of a particular value flowing from one entity to the other. Sankey Diagram For instance, you can analyze traffic flows from one location to the other. Sankey diagrams can help data engineering teams analyze data flows from different platforms or servers. The analysis can help identify bottlenecks, redundancies, and resource constraints for optimization planning. Sunburst Sequence The sunburst sequence visualizes hierarchical data through concentric circles. Each circle represents a level in the hierarchy and has multiple segments. Each segment represents the proportion of data in the hierarchy. Furthermore, it color codes segments to distinguish between categories within a particular hierarchy. Sunburst Sequence For instance, you can visualize the population of different world regions through a sunburst sequence. The innermost circle represents a continent, the middle one shows a particular region, and the outermost circle displays the country within that region. The visualization helps data science teams analyze relationships between nested data structures. The information will allow you to define clear data labels needed for model training. Table A table represents data in a structured format with rows and columns. Databricks offers additional functionality to hide, reformat, and reorder data. Tables help summarize information in structured datasets. You can use them for further analysis through SQL queries. Word Cloud Word cloud visualizations display words in different sizes according to their frequency in textual data. For instance, you can analyze customer comments or feedback and determine overall sentiment based on the highest-occurring words. Word Cloud While word clouds help identify key themes in unstructured textual datasets, they can suffer from oversimplification. Users must use word clouds only as a quick overview and augment textual analysis with advanced natural language processing techniques. Visualization is critical to efficient data management. Find out the top tools for data management for computer vision Visualizations in Databricks: Key Takeaways With an ever-increasing data volume and variety, visualization is becoming critical for quickly communicating data-based insights in a simplified manner. Databricks is a powerful tool with robust visualization types for analyzing complex datasets. Below are a few key points to remember regarding visualization in Databricks. Databricks SQL and Databricks Notebooks: Databricks offers advanced visualizations through Databricks SQL and Databricks Notebooks as a built-in functionality. Visualization configurations: Users can configure multiple visualization settings to produce charts, graphs, maps, and diagrams per their requirements. Visualization types: Databricks offers multiple visualizations, including bar charts, line graphs, pie charts, scatter plots, area graphs, box plots, bubble charts, combo charts, heatmaps, histograms, pivot tables, choropleth maps, funnels, cohort tables, counter display, Sankey diagrams, sunburst sequences, tables, and word clouds.

Mar 28 2024

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Microsoft MORA: Multi-Agent Video Generation Framework

What is Mora? Mora is a multi-agent framework designed for generalist video generation. Based on OpenAI's Sora, it aims to replicate and expand the range of generalist video generation tasks. Sora, famous for making very realistic and creative scenes from written instructions, set a new standard for creating videos that are up to a minute long and closely match the text descriptions given. Mora distinguishes itself by incorporating several advanced visual AI agents into a cohesive system. This lets it undertake various video generation tasks, including text-to-video generation, text-conditional image-to-video generation, extending generated videos, video-to-video editing, connecting videos, and simulating digital worlds. Mora can mimic Sora’s capabilities using multiple visual agents, significantly contributing to video generation. In this article, you will learn: Mora's innovative multi-agent framework for video generation. The importance of open-source collaboration that Mora enables. Mora's approach to complex video generation tasks and instruction fidelity. About the challenges in video dataset curation and quality enhancement. TL; DR Mora's novel approach uses multiple specialized AI agents, each handling different aspects of the video generation process. This innovation allows various video generation tasks, showcasing adaptability in creating detailed and dynamic video content from textual descriptions. Mora aims to fix the problems with current models like Sora, which is closed-source and does not let anyone else use it or do more research in the field, even though it has amazing text-to-video conversion abilities 📝🎬. Unfortunately, Mora still has problems with dataset quality, video fidelity, and ensuring that outputs align with complicated instructions and people's preferences. These problems show where more work needs to be done in the future. OpenAI Sora’s Closed-Source Nature The closed-source nature of OpenAI's Sora presents a significant challenge to the academic and research communities interested in video generation technologies. Sora's impressive capabilities in generating realistic and detailed videos from text descriptions have set a new standard in the field.   Related: New to Sora? Check out our detailed explainer on the architecture, relevance, limitations, and applications of Sora.   However, the inability to access its source code or detailed architecture hinders external efforts to replicate or extend its functionalities. This limits researchers from fully understanding or replicating its state-of-the-art performance in video generation.  Here are the key challenges highlighted due to Sora's closed-source nature: Inaccessibility to Reverse-Engineer Without access to Sora's source code, algorithms, and detailed methodology, the research community faces substantial obstacles in dissecting and understanding the underlying mechanisms that drive its exceptional performance.  This lack of transparency makes it difficult for other researchers to learn from and build upon Sora's advancements, potentially slowing down the pace of innovation in video generation. Extensive Training Datasets Sora's performance is not just the result of sophisticated modeling and algorithms; it also benefits from training on extensive and diverse datasets. But the fact that researchers cannot get their hands on similar datasets makes it very hard to copy or improve Sora's work. High-quality, large-scale video datasets are crucial for training generative models, especially those capable of creating detailed, realistic videos from text descriptions. However, these datasets are often difficult to compile due to copyright issues, the sheer volume of data required, and the need for diverse, representative samples of the real world. Creating, curating, and maintaining high-quality video datasets requires significant resources, including copyright permissions, data storage, and management capabilities. Sora's closed nature worsens these challenges by not providing insights into compiling the datasets, leaving researchers to navigate these obstacles independently. Computational Power Creating and training models like Sora require significant computational resources, often involving large clusters of high-end GPUs or TPUs running for extended periods. Many researchers and institutions cannot afford this much computing power, which makes the gap between open-source projects like Mora and proprietary models like Sora even bigger. Without comparable computational resources, it becomes challenging to undertake the necessary experimentation—with different architectures and hyperparameters—and training regimes required to achieve similar breakthroughs in video generation technology. Learn more about these limitations in the technical paper.   Evolution: Text-to-Video Generation Over the years, significant advancements in text-to-video generation technology have occurred, with each approach and architecture uniquely contributing to the field's growth.  Here's a summary of these evolutionary stages, as highlighted in the discussion about text-to-video generation in the Mora paper: GANs (Generative Adversarial Networks) Early attempts at video generation leveraged GANs, which consist of two competing networks: a generator that creates images or videos that aim to be indistinguishable from real ones, and a discriminator that tries to differentiate between the real and generated outputs. Despite their success in image generation, GANs faced challenges in video generation due to the added complexity of temporal coherence and higher-dimensional data. Generative Video Models Moving beyond GANs, the field saw the development of generative video models designed to produce dynamic sequences. Generating realistic videos frame-by-frame and maintaining temporal consistency is a challenge, unlike in static image generation. Auto-Regressive Transformers Auto-regressive transformers were a big step forward because they could generate video sequences frame-by-frame. These models predicted each new frame based on the previously generated frames, introducing a sequential element that mirrors the temporal progression of videos. But this approach often struggled with long-term coherence over longer sequences. Large-Scale Diffusion Models Diffusion models, known for their capacity to generate high-quality images, were extended to video generation. These models gradually refine a random noise distribution toward a coherent output. They apply this iterative denoising process to the temporal domain of videos. Related: Read our guide on HuggingFace’s Dual-Stream Diffusion Net for Text-to-Video Generation. Image Diffusion U-Net Adapting the U-Net architecture for image diffusion models to video content was critical. This approach extended the principles of image generation to videos, using a U-Net that operates over sequences of frames to maintain spatial and temporal coherence. 3D U-Net Structure The change to a 3D U-Net structure allowed for more nuance in handling video data, considering the extra temporal dimension. This change also made it easier to model time-dependent changes, improving how we generate coherent and dynamic video content. Latent Diffusion Models (LDMs) LDMs generate content in a latent space rather than directly in pixel space. This approach reduces computational costs and allows for more efficient handling of high-dimensional video data. LDMs have shown that they can better capture the complex dynamics of video content. Diffusion Transformers Diffusion transformers (DiT) combine the strengths of transformers in handling sequential data with the generative capabilities of diffusion models. This results in high-quality video outputs that are visually compelling and temporally consistent.  Useful: Stable Diffusion 3 is an example of a multimodal diffusion transformer model that generates high-quality images and videos from text. Check out our explainer on how it works. AI Agents: Advanced Collaborative Multi-agent Structures The paper highlights the critical role of collaborative, multi-agent structures in developing Mora. It emphasizes their efficacy in handling multimodal tasks and improving video generation capabilities.  Here's a concise overview based on the paper's discussion on AI Agents and their collaborative frameworks: Multimodal Tasks Advanced collaborative multi-agent structures address multimodal tasks involving processing and generating complex data across different modes, such as text, images, and videos. These structures help integrate various AI agents, each specialized in handling specific aspects of the video generation process, from understanding textual prompts to creating visually coherent sequences. Cooperative Agent Framework (Role-Playing) The cooperative agent framework, characterized by role-playing, is central to the operation of these multi-agent structures. Each agent is assigned a unique role or function in this framework, such as prompt enhancement, image generation, or video editing.  By defining these roles, the framework ensures that an agent with the best skills for each task is in charge of that step in the video generation process, increasing overall efficiency and output quality. Multi-Agent Collaboration Strategy The multi-agent collaboration strategy emphasizes the orchestrated interaction between agents to achieve a common goal. In Mora, this strategy involves the sequential and sometimes parallel processing of tasks by various agents. For instance, one agent might enhance an initial text prompt, convert it into another image, and finally transform it into a video sequence by yet another. This collaborative approach allows for the flexible and dynamic generation of video content that aligns with user prompts. AutoGen (Generic Programming Framework) A notable example of multi-agent collaboration in practice is AutoGen. This generic programming framework is designed to automate the assembly and coordination of multiple AI agents for a wide range of applications.  Within the context of video generation, AutoGen can streamline the configuration of agents according to the specific requirements of each video generation task to generate complex video content from textual or image-based prompts. Mora drone to butterfly flythrough shot. | Image Source. Role of an AI Agent The paper outlines the architecture involving multiple AI agents, each serving a specific role in the video generation process. Here's a closer look at the role of each AI agent within the framework:   Illustration of how to use Mora to conduct video-related tasks Prompt Selection and Generation Agent This agent is tasked with processing and optimizing textual prompts for other agents to process them further. Here are the key techniques used for Mora: GPT-4: This agent uses the generative capabilities of GPT-4 to generate high-quality prompts that are detailed and rich in context. Prompt Selection: This involves selecting or enhancing textual prompts to ensure they are optimally prepared for the subsequent video generation process. This step is crucial for setting the stage for generating images and videos that closely align with the user's intent. Good Read: Interested in GPT-4 Vision alternatives? Check out our blog post. Text-to-Image Generation Agent This agent uses a retrained large text-to-image model to convert the prompts into initial images. The retraining process ensures the model is finely tuned to produce high-quality images, laying a strong foundation for the video generation process. Image-to-Image Generation Agent  This agent specializes in image-to-image generation, taking initial images and editing them based on new prompts or instructions. This ability allows for a high degree of customization and improvement in video creation. Image-to-Video Generation Agent This agent transforms static images into dynamic video sequences, extending the visual narrative by generating coherent frames. Here are the core techniques and models: Core Components: It incorporates two pre-trained models: GPT-3 for understanding and generating text-based instructions, and Stable Diffusion for translating these instructions into visual content. Prompt-to-Prompt Technique: The prompt-to-prompt technique guides the transformation from an initial image to a series of images that form a video sequence. Classifier-Free Guidance: Classifier-free guidance is used to improve the fidelity of generated videos to the textual prompts so that the videos remain true to the users' vision. Text-to-Video Generation Agent: This role is pivotal in transforming static images into dynamic videos that capture the essence of the provided descriptions. Stable Video Diffusion (SVD) and Hierarchical Training Strategy: A model specifically trained to understand and generate video content, using a hierarchical training strategy to improve the quality and coherence of the generated videos. Video Connection Agent This agent creates seamless transitions between two distinct video sequences for a coherent narrative flow. Here are the key techniques used: Pre-Trained Diffusion-Based T2V Model: This model uses a pre-trained diffusion-based model specialized in text-to-video (T2V) tasks to connect separate video clips into a cohesive narrative. Text-Based Control: This method uses textual descriptions to guide the generation of transition videos that seamlessly connect disparate video clips, ensuring logical progression and thematic consistency. Image-to-Video Animation and Autoregressive Video Prediction: These capabilities allow the agent to animate still images into video sequences, predict and generate future video frames based on previous sequences, and create extended and coherent video narratives. Mora’s Video Generation Process Mora's video-generation method is a complex, multi-step process that uses the unique capabilities of specialized AI agents within its framework. This process allows Mora to tackle various video generation tasks, from creating videos from text descriptions to editing and connecting existing videos.  Here's an overview of how Mora handles each task: Mora’s video generation process. Text-to-Video Generation This task begins with a detailed textual prompt from the user. Then, the Text-to-Image Generation Agent converts the prompts into initial static images. These images serve as the basis for the Image-to-Video Generation Agent, which creates dynamic sequences that encapsulate the essence of the original text and produce a coherent video narrative. Text-Conditional Image-to-Video Generation This task combines textual prompts with a specific starting image. Mora first improves the input with the Prompt Selection and Generation Agent, ensuring that the text and image are optimally prepared for video generation.  Then, the Image-to-Video Generation Agent takes over, generating a video that evolves from the initial image and aligns with the textual description. Extend Generated Videos To extend an existing video, Mora uses the final frame of the input video as a launchpad. The Image-to-Video Generation Agent crafts additional sequences that logically continue the narrative from the last frame, extending the video while maintaining narrative and visual continuity. Video-to-Video Editing In this task, Mora edits existing videos based on new textual prompts. The Image-to-Image Generation Agent first edits the video's initial frame according to the new instructions. Then, the Image-to-Video Generation Agent generates a new video sequence from the edited frame, adding the desired changes to the video content. Connect Videos Connecting two videos involves creating a transition between them. Mora uses the Video Connection Agent, which analyzes the first video's final frame and the second's initial frame. It then generates a transition video that smoothly links the two segments into a cohesive narrative flow. Simulating Digital Worlds Mora generates video sequences in this task that simulate digital or virtual environments. The process involves appending specific style cues (e.g., "in digital world style") to the textual prompt, guiding the Image-to-Video Generation Agent to create a sequence reflecting the aesthetics of a digital realm.  This can involve stylistically transforming real-world images into digital representations or generating new content within the specified digital style. See Also: Read our explainer on Google’s Video Gaming Companion: Scalable Instructable Multiworld Agent [SIMA].   Mora: Experimental Setup As detailed in the paper, the experimental setup for evaluating Mora is comprehensive and methodically designed to assess the framework's performance across various dimensions of video generation. Here's a breakdown of the setup: Baseline The baseline for comparison includes existing open-sourced models that showcase competitive performance in video generation tasks. These models include Videocrafter, Show-1, Pika, Gen-2, ModelScope, LaVie-Interpolation, LaVie, and CogVideo.  These models are a reference point for evaluating Mora's advancements and position relative to the current state-of-the-art video generation. Basic Metrics The evaluation framework comprises several metrics to quantify Mora's performance across different dimensions of video quality and condition consistency: Video Quality Measurement Object Consistency: Measures the stability of object appearances across video frames. Background Consistency: Assesses the uniformity of the background throughout the video. Motion Smoothness: Evaluates the fluidity of motion within the video. Aesthetic Score: Gauges the artistic and visual appeal of the video. Dynamic Degree: Quantifies the video's dynamic action or movement level. Imaging Quality: Assesses the overall visual quality of the video, including clarity and resolution. Video Condition Consistency Metric Temporal Style: Measures how consistently the video reflects the temporal aspects (e.g., pacing, progression) described in the textual prompt. Appearance Style: Evaluates the adherence of the video's visual style to the descriptions provided in the prompt, ensuring that the generated content matches the intended appearance. Self-Defined Metrics Video-Text Integration (VideoTI): Measures the model’s fidelity to textual instructions by comparing text representations of input images and generated videos. Temporal Consistency (TCON): Evaluates the coherence between an original video and its extended version, providing a metric for assessing the integrity of extended video content. Temporal Coherence (Tmean): Quantifies the correlation between the intermediate generated and input videos, measuring overall temporal coherence. Video Length: This parameter quantifies the duration of the generated video content, indicating the model's capacity for producing videos of varying lengths. Implementation Details The experiments use high-performance hardware, specifically TESLA A100 GPUs with substantial VRAM. This setup ensures that Mora and the baseline models are evaluated under conditions allowing them to fully express their video generation capabilities. The choice of hardware reflects the computational intensity of training and evaluating state-of-the-art video generation models. Mora video generation - Fish underwater flythrough Limitations of Mora The paper outlines several limitations of the Mora framework. Here's a summary of these key points: Curating High-Quality Video Datasets Access to high-quality video datasets is a major challenge for training advanced video generation models like Mora. Copyright restrictions and the sheer volume of data required make it difficult to curate diverse and representative datasets that can train models capable of generating realistic and varied video content. Read Also: The Full Guide to Video Annotation for Computer Vision.   Quality and Length Gaps While Mora demonstrates impressive capabilities, it has a noticeable gap in quality and maximum video length compared to state-of-the-art models like Sora. This limitation is particularly evident in tasks requiring the generation of longer videos, where maintaining visual quality and coherence becomes increasingly challenging. Simulating videos in Mora vs in Sora. Instruction Following Capability Mora sometimes struggles to precisely follow complex or detailed instructions, especially when generating videos that require specific actions, movements, or directionality. This limitation suggests that further improvement in understanding and interpreting textual prompts is needed. Human Visual Preference Alignment The experimental results may not always align with human visual preferences, particularly in scenarios requiring the generation of realistic human movements or the seamless connection of video segments. This misalignment highlights the need to incorporate a more nuanced understanding of physical laws and human dynamics into the video-generation process. Mora Vs. Sora: Feature Comparisons The paper compares Mora and OpenAI's Sora across various video generation tasks. Here's a detailed feature comparison based on their capabilities in different aspects of video generation: Check out the project repository on GitHub. Mora Multi-Agent Framework: Key Takeaways The paper "Mora: Enabling Generalist Video Generation via a Multi-Agent Framework" describes Mora, a new framework that advances video technology. Using a multi-agent approach, Mora is flexible and adaptable across various video generation tasks, from creating detailed scenes to simulating complex digital worlds. Because it is open source, it encourages collaboration, which leads to new ideas, and lets the wider research community add to and improve its features. Even though Mora has some good qualities, it needs high-quality video datasets, video quality, length gaps, trouble following complicated instructions correctly, and trouble matching outputs to how people like to see things. Finding solutions to these problems is necessary to make Mora work better and be used in more situations.  Continuing to improve and develop Mora could change how we make video content so it is easier for creators and viewers to access and have an impact.

Mar 26 2024

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Panoptic Segmentation Updates in Encord

Panoptic Segmentation Updates in Encord Over the past 6 months, we have updated and built new features within Encord with a strong focus on improving your panoptic segmentation workflows across data, labeling, and model evaluation. Here are some updates we’ll cover in this article: Bitmask lock. SAM + Bitmask lock + Brush for AI-assisted precision labeling. Fast and performant rendering of fully bitmask-segmented images and videos. Panoptic Quality model evaluation metrics. Bitmask Lock within Encord Annotate to Manage Segmentation Overlap Our Bitmask Lock feature introduces a way to prevent segmentation and masks from overlapping, providing pixel-perfect accuracy for your object segmentation tasks. By simply toggling the “Bitmask cannot be drawn over” button, you can prevent any part of a bitmask label from being included in another label. This feature is crucial for applications requiring precise object boundaries and pixel-perfect annotations, eliminating the risk of overlapping segmentations. Let’s see how to do this within Encord Annotate: Step 1: Create your first Bitmask Initiating your labeling process with the Bitmask is essential for creating precise object boundaries. If you are new to the Bitmask option, check out our quickstart video walkthrough on creating your first Bitmask using brush tools for labeling. Step 2: Set Bitmask Overlapping Behavior  Managing how bitmasks overlap is vital for ensuring accurate segmentation, especially when dealing with multiple objects that are close to each other or overlapping. After creating your first bitmask, adjust the overlapping behavior settings to dictate how subsequent bitmasks interact with existing ones. This feature is crucial for delineating separate objects without merging their labels—perfect for panoptic segmentation. This prevents any part of this bitmask label from being included in another label. This is invaluable for creating high-quality datasets for training panoptic segmentation models. Step 3: Lock Bitmasks When Labeling Multiple Instances Different images require different approaches. Beyond HSV, you can use intensity values for grayscale images (like DICOM) or RGB for color-specific labeling. This flexibility allows for tailored labeling strategies that match the unique attributes of your dataset. Experiment with the different settings (HSV, intensity, and RGB) to select the best approach for your specific labeling task. Adjust the criteria to capture the elements you need precisely. Step 4: Using the Eraser Tool Even with careful labeling, adjustments may be necessary. The eraser tool can remove unwanted parts of a bitmask label before finalizing it, providing an extra layer of precision. If you've applied a label inaccurately, use the eraser tool to correct any errors by removing unwanted areas of the bitmask. See our documentation to learn more. Bitmask-Segmented Images and Videos Got a Serious Performance Lift (At Least 5x) Encord's commitment to enhancing user experience and efficiency is evident in the significant performance improvements made to the Bitmask-segmented annotation within the Label Editor. Our Engineering team has achieved a performance lift of at least 5x by directly addressing user feedback and pinpointing critical bottlenecks. This improves how fast the editor loads for your panoptic segmentation labeling instances.  Here's a closer look at the differences between the "before" and "after" scenarios, highlighting the advancements: Before the Performance Improvements: Performance Lag on Zoom: Users experienced small delays when attempting to zoom in on images, with many instances (over 100) that impacted the precision and speed of their labeling process. Slow Response to Commands: Basic functionalities like deselecting tools or simply navigating through the label editor were met with sluggish responses. Operational Delays: Every action, from image loading to applying labels, was hindered by "a few milliseconds" of delay, which accumulated significant time overheads across projects. After the Performance Enhancements: Quicker Image Load Time: The initial step of image loading has seen a noticeable speed increase! This sets a good pace for the entire labeling task. Responsiveness: The entire label editor interface, from navigating between tasks to adjusting image views, is now remarkably more responsive. This change eradicates previous lag-related frustrations and allows for a smoother user experience. Improved Zoom Functionality: Zooming in and out has become significantly more fluid and precise. This improvement is precious for detailed labeling work, where accuracy is paramount. The positive changes directly result from the Engineering team's responsiveness to user feedback. Our users have renewed confidence in handling future projects with the Label Editor. We are dedicated to improving Encord based on actual user experiences. Use Segment Anything Model (SAM) and Bitmask Lock for High Annotation Precision Starting your annotation process can be time-consuming, especially for complex images. Our Segment Anything Model (SAM) integration offers a one-click solution to create initial annotations. SAM identifies and segments objects in your image, significantly speeding up the annotation process while ensuring high accuracy. Step 1: Select the SAM tool from the toolbar with the Bitmask Lock enabled.  Step 2: Click on the object you wish to segment in your image. SAM will automatically generate a precise bitmask for the object. Step 3: Use the bitmask brush to refine the edges for pixel-perfect segmentation if needed. See how to use the Segment Anything Model (SAM) within Encord in our documentation.   Validate Segmentation with Panoptic Quality Metrics You can easily evaluate your segmentation model’s panoptic mask quality with new metrics:  mSQ (mean Segmentation Quality) mRQ (mean Recognition Quality) mPQ (mean Panoptic Quality) The platform will calculate mSQ, mRQ, and mPQ for your predictions, labels, and dataset to clearly understand the segmentation performance and areas for improvement. Navigate to Active → Under the Model Evaluation tab, choose the panoptic model you want to evaluate. Under Display, toggle the Panoptic Quality Metrics (still in beta) option to see the model's mSQ, mRQ, and mPQ scores. Fast Rendering of Fully Bitmask-Segmented Images within Encord Active The performance improvement within the Label Editor also translates to how you view and load panoptic segmentation within Active.  Try it yourself: Key Takeaways: Panoptic Segmentation Updates in Encord Here’s a recap of the key features and improvements within Encord that can improve your Panoptic Segmentation workflows across data and models: Bitmask Lock: This feature prevents overlaps in segmentation. it guarantees the integrity of each label, enhancing the quality of the training data and, consequently, the accuracy of machine learning models. This feature is crucial for projects requiring meticulous detail and precision. SAM + Bitmask Lock + Brush: The Lock feature allows you to apply Bitmasks to various objects within an image, which reduces manual effort and significantly speeds up your annotation process. The integration of SAM within Encord's platform, using Lock to manage Bitmask overlaps, and the generic brush tool empower you to achieve precise, pixel-perfect labels with minimal effort. Fast and Performant Rendering of Fully Bitmask-segmented Images and Videos: We have made at least 5x improvements to how Encord quickly renders fully Bitmask-segmented images and videos across Annotate Label Editor and Active. Panoptic Quality Model Evaluation Metrics: The Panoptic Quality Metrics—comprising mean Segmentation Quality (mSQ), mean Recognition Quality (mRQ), and mean Panoptic Quality (mPQ)—provide a comprehensive framework for evaluating the effectiveness of segmentation models.

Mar 06 2024

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Qwen-VL and Qwen-VL-Chat: Introduction to Alibaba’s AI Models

Qwen-VL is a series of open-source large vision-language models (LVLMs), offering a potent combination of advanced capabilities and accessibility. As an open-source project, Qwen-VL not only democratizes access to cutting-edge AI technology but also positions itself as a formidable competitor to established models from tech giants like OpenAI’s GPT-4V and Google’s Gemini. In the competitive landscape of LVLMs, Qwen-VL has quickly risen to the forefront, securing its place as a leader on the OpenVLM leaderboard. This leaderboard, which encompasses 38 different VLMs including GPT-4V, Gemini, QwenVLPlus, LLaVA, and others, serves as a comprehensive benchmark for evaluating model performance across 13 distinct multimodal tasks. OpenVLM Leaderboard Qwen-VL's performance across these benchmarks underscores its versatility and robustness in handling various vision-language tasks with unparalleled accuracy and efficiency. By leading the charge on the OpenVLM leaderboard, Qwen-VL sets a new standard for excellence in the field, pushing the boundaries of what is possible with LVLMs and paving the way for future advancements in multimodal AI research. Introduction to Large-scale Vision Language Models (LVLMs) Large Language Models (LLMs) have attracted attention in recent years for their remarkable text generation and comprehension capabilities in the field of generative AI. However, their limitation to processing text alone has constrained their utility in various applications. In response to this limitation, a new class of models known as Large Vision Language Models (LVLMs) has come up, aiming to integrate visual data with textual information to address vision-centric tasks. LVLMs improve conventional LLMs by integrating vision language learning, thus extending their applicability to include image datasets. However, despite their promising potential, open-source LVLM implementations encounter hurdles such as inadequate training and optimization when compared to proprietary models. Also, understanding visual content still remains a significant challenge for existing LVLM frameworks. Overview of Qwen-VL The Qwen-VL series represents a significant advancement in Large Vision Language Models (LVLMs), designed to overcome the limitations of existing models and equip LLMs with visual processing capabilities. Built upon the Alibaba Cloud’s 7 billion parameter model, Qwen-7B language model, the Qwen-VL series introduces a visual receptor architecture comprising a language-aligned visual encoder and a position-aware adapter. This architecture enables Qwen-VL models to effectively process visual inputs, generate responses based on prompts, and perform various vision-language tasks such as image recognition, image captioning, visual question answering, and visual grounding. Qwen-VL models demonstrate leading performance on vision-centric benchmarks and support multiple languages, including English and Chinese. For more information on VLMs, read the blog Guide to Vision-Language Models (VLMs)   Key Features of Qwen-VL Qwen-VL models demonstrate good accuracy on a wide range of vision-centric understanding benchmarks, surpassing other SOTA models of similar scales. They excel not only in conventional benchmarks such as captioning and question-answering but also in recently introduced dialogue benchmarks. Here are the key features of Qwen-VL: Multi-lingual Support: Similar to Qwen-LM, Qwen-VLs are trained on multilingual image-text data, with a substantial corpus in English and Chinese. This enables Qwen-VLs to naturally support English, Chinese, and other multilingual instructions. Multi-image Capability: During training, Qwen-VLs can handle arbitrary interleaved image-text data as inputs, allowing them to compare, understand, and analyze context when multiple images are provided. Fine-grained Visual Understanding: Qwen-VLs exhibit highly competitive fine-grained visual understanding abilities, thanks to their higher-resolution input size and fine-grained corpus used during training. Compared to existing vision-language generalists, Qwen-VLs demonstrate superior performance in tasks such as grounding, text-reading, text-oriented question answering, and fine-grained dialogue comprehension. Vision-centric Understanding: This allows the model to comprehensively interpret and process visual information. With advanced architecture integrating a language-aligned visual encoder and position-aware adapter, Qwen-VL excels in tasks like image captioning, question answering, and visual grounding. Its fine-grained analysis ensures precise interpretation of visual content, making Qwen-VL highly effective in vision-language tasks and real-world applications. Design Structure of Qwen-VL Beginning with the foundation of Qwen-LM, the model is enhanced with visual capacity through several key components: Visual Receptor: Qwen-VL incorporates a carefully designed visual receptor, which includes a visual encoder and adapter. This component is responsible for processing image inputs and extracting fixed-length sequences of image features.  Input-Output Interface: The model's input-output interface is optimized to differentiate between image and text feature inputs. Special tokens are utilized to delineate image feature input, ensuring seamless integration of both modalities. 3-stage Training Pipeline: Qwen-VL employs a sophisticated 3-stage training pipeline to optimize model performance. This pipeline encompasses comprehensive training stages aimed at fine-tuning the model's parameters and enhancing its ability to comprehend and generate responses for both text and image inputs. Multilingual Multimodal Cleaned Corpus: Qwen-VL is trained on a diverse multilingual multimodal corpus, which includes cleaned data encompassing both textual and visual information. This corpus facilitates the model's ability to understand and generate responses in multiple languages while effectively processing various types of visual content. Model Architecture of Qwen-VL The architecture of Qwen-VL comprises three key components, each contributing to the model's robustness in processing both text and visual inputs.  Large Language Model Qwen-VL leverages a large language model as its foundational component. This machine learning model is initialized with pre-trained weights obtained from Qwen-7B, ensuring a strong linguistic foundation for the model's language processing capabilities. Visual Encoder Qwen-VL employs the Vision Transformer (ViT) architecture, utilizing pre-trained weights from Openclip's ViT-bigG. During both training and inference, input images are resized to a specific resolution. The visual encoder processes these images by dividing them into patches with a stride of 14, thereby generating a set of image features that encapsulate visual information. Position-aware Vision-Language Adapter To address efficiency concerns arising from long sequences of image features, Qwen-VL introduces a vision-language adapter. This adapter is designed to compress the image features, enhancing computational efficiency. It consists of a single-layer cross-attention module initialized randomly. This module utilizes a group of trainable embeddings as query vectors and the image features from the visual encoder as keys for cross-attention operations. By employing this mechanism, the visual feature sequence is compressed to a fixed length of 256. To preserve positional information crucial for fine-grained image comprehension, 2D absolute positional encodings are incorporated into the query-key pairs of the cross-attention mechanism. This ensures that positional details are retained during the compression process. The compressed image feature sequence of length 256 is then fed into the large language model, enabling Qwen-VL to effectively process both textual and visual inputs and perform a wide range of vision-language tasks with high accuracy and efficiency. Training Pipeline of Qwen-VL series For more information, read the official paper released on Arxiv: Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.   Performance of Qwen-VL against State-of-The-Art LVLMs The performance of Qwen-VL models, particularly Qwen-VL-Max, surpasses SOTA models such as Gemini Ultra and GPT-4V in various text-image multimodal tasks. Compared to the open-source version of Qwen-VL, these models achieve comparable results to Gemini Ultra and GPT-4V, while significantly outperforming previous best results from open-source models. Performance of Qwen-VL-Plus and Qwen-VL-Max against other LVLM In particular, Qwen-VL-Max demonstrates superior performance over GPT-4V from OpenAI and Gemini from Google in tasks related to Chinese question answering and Chinese text comprehension. This achievement highlights the advanced capabilities of Qwen-VL-Max and its potential to establish new benchmarks in multimodal AI research and application. It should also be noted that most SOTA models are not trained on chinese language. Capabilities of Qwen-VL Qwen-VL exhibits a diverse range of capabilities that enable it to effectively comprehend and interact with visual and textual information, as well as reason and learn from its environment. These capabilities include: Basic Recognition Capabilities Qwen-VL demonstrates strong basic recognition capabilities, accurately identifying and describing various elements within images, including common objects, celebrities, landmarks, and intricate details. Recognition capabilities of Qwen-VL Visual Agent Capability As a visual agent, Qwen-VL is capable of providing detailed background information, answering questions, and analyzing complex visual content. It can also compose poetry in multiple languages inspired by visual stimuli and analyze everyday screenshots. Visual Agent Capabilities of Qwen-VL Visual Reasoning Capability Qwen-VL possesses advanced visual reasoning capabilities, extending beyond content description to comprehend and interpret intricate representations such as flowcharts, diagrams, and other symbolic systems. It excels in problem-solving and reasoning tasks, including mathematical problem-solving and profound interpretations of charts and graphs. Qwen-VL has advanced visual reasoning capabilities Text Information Recognition and Processing Qwen-VL exhibits enhanced text information recognition and processing abilities, efficiently extracting information from tables and documents, reformatting it to meet customized output requirements, and effectively identifying and converting dense text. It also supports images with extreme aspect ratios, ensuring flexibility in processing diverse visual content. Advanced text information recognition and processing abilities of Qwen-VL Few-shot Learning on Vision-Language Tasks Qwen-VL demonstrates satisfactory in-context learning (few-shot learning) ability, achieving superior performance on vision-language tasks such as question answering and image captioning compared to models with similar numbers of parameters. Its performance rivals even larger models, showcasing its adaptability and efficiency in learning from limited data. For more information on few-shot learning, read the blog Few Shot Learning in Computer Vision: Approaches & Uses   Qwen-VL Availability Qwen-VL, including Qwen-VL-Plus and Qwen-VL-Max, is now readily accessible through various platforms, offering researchers and developers convenient access to its powerful capabilities: HuggingFace: Users can access Qwen-VL-Plus and Qwen-VL-Max through the Huggingface Spaces and Qwen website, enabling seamless integration into their projects and workflows. Dashscope APIs: The APIs of Qwen-VL-Plus and Qwen-VL-Max are available through the Dashscope platform, providing developers with the flexibility to leverage its capabilities for their AI applications. Detailed documentation and quick-start guides are available on the Dashscope platform for easy integration. QianWen Web Portal: By logging into the Tongyi QianWen web portal and switching to "Image Understanding" mode, users can harness the latest Qwen-VL-Max capabilities for image understanding tasks. This mode offers additional functionalities tailored specifically for image processing and understanding. ModelScope: The Qwen-VL-Chat demo is available on modelscope. GitHub Repository: The code and model weights of both Qwen-VL and Qwen-VL-Chat are openly available to download on GitHub, allowing researchers and developers to explore, modify, and utilize them freely. The commercial use of these resources is permitted, enabling their integration into commercial projects and applications. Qwen-VL-Chat Qwen-VL-Chat, as a generalist multimodal LLM-based AI assistant, supports complex interactions, including multiple image inputs, multi-round question answering, and creative capabilities. Unlike traditional vision-language chatbots, Qwen-VL-Chat's alignment techniques enable it to comprehend and respond to complex visual and textual inputs with superior accuracy and flexibility. Here's how Qwen-VL-Chat stands out in real-world dialog benchmarks and compares with existing models: Qwen-VL-Chat Vs. Vision-Language Chat Performance of Qwen-VL against other generalist models across various tasks Qwen-VL-Chat's advanced capabilities are evaluated using the TouchStone benchmark, which assesses overall text-image dialogue capability and alignment with humans. Unlike conventional models like chatGPT or Bard, Qwen-VL-Chat excels in handling direct image input, thanks to fine-grained image annotations provided by human labeling. With a comprehensive coverage of 300+ images, 800+ questions, and 27 categories, including attribute-based Q&A, celebrity recognition, writing poetry, summarizing multiple images, product comparison, and math problem solving, Qwen-VL-Chat achieves superior performance in understanding and responding to complex visual and textual inputs. You can find the official tutorial to implement Qwen-VL-Chat on your own on Github.   Real-world Dialog Benchmark Qwen-VL-Chat's outstanding results in other multimodal benchmarks, such the MME Benchmark and Seed-Bench, demonstrate that its performance evaluation extends beyond the TouchStone benchmark. In both the perceptual and cognition tracks, Qwen-VL-Chat obtains state-of-the-art scores in the MME Benchmark, an extensive evaluation of multimodal large language models. The Qwen series, which includes Qwen-VL-Chat, achieves state-of-the-art performance in Seed-Bench, a benchmark consisting of 19K multiple-choice questions with precise human annotations. Qwen-VL: What’s Next? The release of the Qwen-VL series represents a significant stride forward in large-scale multilingual vision-language models, with the goal of advancing multimodal research.  Qwen-VL has demonstrated its superiority over comparable artificial intelligence models across various benchmarks, facilitating multilingual complex conversations, multi-image interleaved conversations, grounding in Chinese, and fine-grained recognition. Looking ahead, the focus is on further enhancing Qwen-VL's capabilities in several key dimensions: Multi-modal Generation The team plans to integrate Qwen-VL with more modalities, including speech and video. By expanding its scope to encompass these modalities, Qwen-VL will enhance its ability to understand and generate content across a wider range of inputs. Multi-Modal Generation This generative AI model will be further developed to excel in multi-modal generation, particularly in generating high-fidelity images and fluent speech. By enhancing its ability to generate content across multiple modalities with high fidelity and fluency, Qwen-VL will advance the state-of-the-art in multimodal AI systems. Augmentation of Model Size and Training Data Efforts are underway to scale up the model size, training data, and resolution of Qwen-VL. This enhancement aims to enable Qwen-VL to handle more complex and intricate relationships within multimodal data, leading to more nuanced and comprehensive understanding and generation of content.

Feb 29 2024

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Gemini 1.5: Google's Generative AI Model with Mixture of Experts Architecture

In December 2023, Google launched the Gemini 1.0 family of models that outperformed state-of-the-art (SoTA) models in multimodal AI capabilities. Fast-forward to February 2024, and the Google Deepmind research team has launched Gemini 1.5 Pro with up to 10 million context windows! Not only that, it maintains near-perfect across the entire context and uses a mixture-of-experts (MoE) architecture for more efficient training & higher-quality responses. In this article, you will learn about:  The superior performance benchmarks of Gemini 1.5 Why it performs better than SoTA at textual, visual, and audio capabilities How well it handles long-context tasks, especially with MoE as it’s architectural backbone How you can get started using it Before we jump into it, let’s set the tone with an overview of the MoE architecture that backs Gemini 1.5. TL;DR Gemini 1.5 is a Sparse mixture-of-experts (MoE) multimodal model with a context window of up to 10 million tokens. It excels at long-term recall and retrieval; generalizes zero-shot to long instructions like analyzing 3 hours of video, and 22 hours of audio with near-perfect recall. It performs better than Gemini 1.0 Pro and 1.0 Ultra but performs worse than 1.0 Ultra for audio and vision. Although there are no detailed insights on the model size, architectural experiments, or the number of experts, the model performs well at in-context memorization and generalization Mixture-of-Experts (MoE) Architecture Gemini 1.5 Pro uses a mixture-of-experts (MoE) architecture for efficient training & higher-quality responses, building on a long line of Google research efforts on sparse models. At its core, MoE diverges from traditional deep learning and Transformer architectures by introducing a dynamic routing mechanism that selectively activates different subsets of parameters (referred to as "experts") depending on the input data. It learns to selectively activate only the most relevant expert pathways in its neural network for nuanced and contextually aware outputs. This approach enables the model to scale more effectively in terms of computational efficiency and capacity without a linear increase in computational demands. In the context of Gemini 1.5, the MoE architecture contributes to efficient training and serving. Concentrating computational resources on the most relevant parts of the model for each input allows for faster convergence and improved performance without necessitating the proportional increase in computational power typically associated with scaling up the model size.   Gemini 1.5 - Model Functionalities Gemini 1.5 drops with some impressive functionalities that beat SoTA models: Huge context window that spans up to 10 million-token context length Reduced training compute with the mixture-of-experts architecture Superior performance compared to Gemini 1.0 models, GPT-4, and other SoTA Huge Context Window A model’s “context window” comprises tokens, the building blocks for processing a user’s query. Tokens can be entire parts or subsections of words, images, videos, audio, or code. The bigger a model’s context window, the more information it can take in and process at a given prompt. Gemini 1.5 is a highly capable multimodal model with token context lengths ranging from 128K to 1 million token context lengths for production applications and up to 10 million for research. This unlocks a lot of use cases: Across reasoning about long text documents Making sense of an hour of video (full movies) 11 hours of audio Entire podcast series 700,000 words 30,000 lines of code simultaneously These capabilities are several times greater than other AI models, including OpenAI’s GPT-4, which powers ChatGPT. Context lengths of foundation models with Gemini 1.5 scaling up to 10 million tokens in research Reduced Training Compute The training compute required to train Gemini 1.5 were TPUv4 accelerators of multiple 4096-chip pods. This underscored the model's reliance on high-performance computing resources to perform well, but it also needed training efficiency techniques with the MoE architecture to be optimal. Gemini 1.5 significantly reduced compute requirements for training despite the larger context windows. This achievement is pivotal in the progress of AI model training efficiency, addressing one of the most pressing challenges in the field: the environmental and economic costs associated with training large-scale AI models. The reduction in training compute is primarily down to the Mixture-of-Experts (MoE) architectural backbone, which Gemini 1.5 uses to optimize computational resources. Beyond that, Gemini 1.5 incorporates state-of-the-art techniques such as sparsity in the model's parameters, which means that only a subset of the model's weights is updated during each training step. This approach reduces the computational load, leading to faster training times and lower energy consumption.  According to the technical report, combining those processes to train the model led to remarkable performance without the proportional increase in resource consumption typically seen in less advanced models. Recalling and Reasoning Google Gemini 1.5 Pro sets a new standard in AI's ability to recall and reason across extensive multimodal contexts. The ten million-token context window—the largest of any foundational model, so far—enables Gemini 1.5 Pro to demonstrate unparalleled proficiency in synthesizing and interpreting vast amounts of information. Gemini 1.5 Pro achieves near-perfect recall in complex retrieval tasks across long text documents, videos, and audio, which shows its understanding of the input. In tests from the report, Gemini 1.5 Pro learned new languages from sparse instructional materials 🤯. This model's proficiency in recalling specific details from large datasets and its capability to apply this knowledge in reasoning tasks usher in a new era in AI applications—ranging from academic research and comprehensive code analysis to nuanced content creation. Superior Performance Benchmark Gemini 1.5 Pro demonstrates remarkable improvements over state-of-the-art (SotA) models, including GPT-4V, in tasks spanning text, code, vision, and audio. Some of the benchmarks for which Gemini 1.5 Pro achieves SotA accuracy include 1H-VideoQA and EgoSchema. This indicates Gemini 1.5 Pro's advanced long-context multimodal understanding. Learn more about how OpenAI’s GPT-Vision is expected to compare to the Gemini family of models in our explainer blog post.    In core text evaluations, Gemini 1.5 Pro consistently outperforms its predecessors (Gemini 1.0 Pro and Ultra) in various domains such as Math, Science & Reasoning, Coding, Multilinguality, and Instruction Following. The model shows substantial improvements, particularly in Math and Science Reasoning, where it outperforms Gemini 1.0 Ultra, and in Coding tasks, it sets a new SotA accuracy benchmark on EgoSchema. Gemini 1.5 Pro's performance in multilingual evaluations highlights its enhanced ability to process and understand multiple languages. It shows significant improvements over both Gemini 1.0 models and other specialist models like USM and Whisper in speech understanding tasks. Needle In A Haystack (NIAH) Evaluation The Needle In A Haystack (NIAH) evaluation showcases Gemini 1.5 Pro's capability to retrieve specific information ("needle") from a massive amount of data ("haystack") across different modalities. This evaluation underscores the model's efficiency in long-context understanding and recall accuracy. Gemini 1.5 Pro achieves near-perfect “needle” recall (>99.7%) up to 1M tokens of “haystack” in all modalities (i.e., text, video audio) and maintains this recall performance when extending to 10 M tokens across modalities Context Window - Text Modality: Recall to Token Count Gemini 1.5 Pro excels in the text modality, with the model achieving over 99% recall for up to 10 million tokens, or approximately 7 million words. This capacity for deep, nuanced understanding and recall from vast quantities of text sets a new benchmark for AI performance in natural language processing. It can sift through large volumes of text to find specific information. Text needle-in-a-haystack task comparison between Gemini 1.5 Pro and GPT-4 Turbo The model demonstrates high recall rates for identifying exact text segments within extensive documents. Context Window - Audio Modality: Recall to Token Count Gemini 1.5 Pro demonstrates an exceptional ability to recall information from audio data, achieving near-perfect recall (>99.7%) up to 2 million tokens, equivalent to approximately 22 hours of audio content. It was able to recall and identify specific audio segments ("needles") embedded within long audio streams ("haystacks").  Audio version of the needle-in-a-haystack experiment comparing Gemini 1.5 Pro and a combination of Whisper and GPT-4 Turbo This represents a significant advancement over combining two SoTA models like Whisper + GPT-4 Turbo in a recall-to-token count comparison, which struggles with long-context audio processing. Context Window - Video Modality: Recall to Token Count Gemini 1.5 Pro maintains high recall performance in the video modality, successfully retrieving information from video data up to 2.8 million tokens, correlating to around 3 hours of video content. The "Video Needle In A Haystack" task tested the model's performance in recalling specific video frames from lengthy videos. This is critical for tasks requiring detailed understanding and analysis of long-duration video sequences. It can accurately pinpoint and recall specific moments or information from extensive video sequences. Multineedle in Haystack Test The researchers created a generalized version of the needle in a haystack test, where the model must retrieve 100 different needles hidden in the context window.  The results? Gemini 1.5 Pro’s performance was above that of GPT-4 Turbo at small context lengths and remains relatively steady across the entire 1M context window. At the same time, the GPT-4 Turbo model drops off more quickly (and cannot go past 128k tokens). Multineedle in Haystack Test Textual Capabilities of Gemini 1.5 Mathematical and Scientific Textual Reasoning Gemini 1.5 Pro shows a +28.9% improvement over Gemini 1.0 Pro and a +5.2% improvement over Gemini 1.0 Ultra. This indicates a substantial increase in its ability to handle complex reasoning and problem-solving tasks. This proficiency is attributed to its extensive training dataset, which includes a wide array of scientific literature and mathematical problems, so the model can grasp and apply complex concepts accurately. Coding In Coding tasks, Gemini 1.5 Pro marked a +8.9% improvement over 1.0 Pro and +0.2% over 1.0 Ultra, showcasing its superior algorithmic understanding and code generation capabilities. The model can 𝐚𝐜𝐜𝐮𝐫𝐚𝐭𝐞𝐥𝐲 𝐚𝐧𝐚𝐥𝐲𝐳𝐞 an entire code library in a single prompt, without the need to fine-tune the model, including understanding and reasoning over small details that a developer might easily miss. Problem Solving Capability across 100,633 lines of code Instructional Understanding Gemini 1.5 Pro excels in Instruction Following, surpassing the 1.0 series in comprehending and executing complex (+9.2% over 1.0 Pro and +2.5% over 1.0 Ultra), multi-step instructions across various data formats and tasks. This indicates its advanced natural language understanding and ability to process and apply knowledge in a contextually relevant manner. Multilinguality The model also shows improvements in handling multiple languages, with a +22.3% improvement over 1.0 Pro and a slight +6.7% improvement over 1.0 Ultra. This highlights its capacity for language understanding and translation across diverse linguistic datasets. This makes it an invaluable tool for global communication and preserving and revitalizing endangered languages. Kalamang has almost no online presence. Machine Translation from One Book (MTOB: arxiv.org/abs/2309.16575) is a recently introduced benchmark evaluating the ability of a learning system to learn to translate Kalamang from just a single book. Gemini 1.5 Pro still translates the user prompt with astonishing accuracy. Visual Capabilities of Gemini 1.5 The model's multimodal understanding is outstanding in Image and Video Understanding tasks. Gemini 1.5 Pro's performance in these areas reflects its ability to interpret and analyze visual data, making it an indispensable tool for tasks requiring a nuanced understanding of text and media. Image and Video Understanding For image understanding, there's a +6.5% improvement over 1.0 Pro but a -4.1% difference compared to 1.0 Ultra. In video understanding, however, Gemini 1.5 Pro shows a significant +16.9% improvement over 1.0 Pro and +3.8% over 1.0 Ultra, indicating robust enhancements in processing and understanding visual content.  These are some areas Gemini 1.5 performs great at: Contextual Understanding: Gemini 1.5 integrates visual data with textual descriptions, enabling it to understand the context and significance of visual elements in a comprehensive manner. This allows for nuanced interpretations that go beyond mere object recognition. Video Analysis: For video content, Gemini 1.5 demonstrates an advanced ability to track changes over time, recognize patterns, and predict outcomes. This includes understanding actions, events, and even the emotional tone of scenes and providing detailed analyses of video data. Image Processing: In image understanding, Gemini 1.5 utilizes state-of-the-art techniques to analyze and interpret images. This includes recognizing and categorizing objects, understanding spatial relationships, and extracting meaningful information from still visuals. Audio Capabilities of Gemini 1.5 Speech Recognition and Translation In an internal YouTube video-based benchmark, Gemini 1.5 Pro was evaluated on 15-minute segments, showing a remarkable ability to understand and transcribe speech with a word error rate (WER) significantly lower than that of its predecessors and other contemporary models.  This capability is especially notable given the challenges posed by long audio segments, where the model maintains high accuracy without the need for segmentation or additional preprocessing. Gemini 1.5 Pro also performed well at translating spoken language from one language to another, maintaining the meaning and context of the original speech. This is particularly important for applications that require real-time or near-real-time translation. Overall, there are mixed results in the audio domain, with a +1.2% improvement in speech recognition over 1.0 Pro but a -5.0% change compared to 1.0 Ultra. In speech translation, Gemini 1.5 Pro shows a slight +0.3% improvement over 1.0 Pro but a -2.2% difference compared to 1.0 Ultra. Gemini 1.5 Core capabilities performance over its predecessor, Gemini 1.0 series of models, Gemini 1.0 Pro and Gemini 1.0 Ultra Long Context Understanding Gemini 1.5 Pro significantly expands the context length to multiple millions of tokens, enabling the model to process larger inputs effectively. This is a substantial improvement over models like Claude 2.1, which has a 200k token context window. Gemini 1.5 Pro maintains a 100% recall at 200k tokens and shows minimal reduction in recall up to 10 million tokens, highlighting its superior ability to manage and analyze extensive data sets. In one example, the model analyzed long, complex text documents, like Victor Hugo’s five-volume novel “Les Misérables” (1382 pages, 732k tokens). The researchers demonstrated multimodal capabilities by coarsely sketching a scene and saying, “Look at the event in this drawing. What page is this on?” With the entire text of Les Misérables in the prompt (1382 pages, 732k tokens), Gemini 1.5 Pro can identify and locate a famous scene from a hand-drawn sketch In another example, Gemini 1.5 Pro analyzed and summarized the 402-page transcripts from Apollo 11’s mission to the moon. “One small step for man, one giant leap for mankind.” Demo of Long Context Understanding Prompt In-Context Learning and the Machine Translation from One Book (MTOB) Benchmark Gemini 1.5 Pro can adapt and generate accurate responses based on minimal instruction. This capability is especially evident in complex tasks requiring understanding nuanced instructions or learning new concepts from a limited amount of information in the prompt. Gemini 1.5 Pro's in-context learning capabilities show its performance on the challenging Machine Translation from One Book (MTOB) benchmark. This benchmark tests the model's ability to learn to translate a new language from a single source of instructional material.  In the MTOB benchmark, Gemini 1.5 Pro was tasked with translating between English and Kalamang, a language with a limited online presence and fewer than 200 speakers. Despite these challenges, the report showed that Gemini 1.5 Pro achieved translation quality comparable to that of human learners with the same instructional materials.  This underscores the model's potential to support language learning and translation for underrepresented languages, opening new avenues for research and application in linguistics and beyond. Gemini 1.5 Pro Vs. Gemini Ultra While Gemini 1.5 Pro (2024) and Gemini Ultra (2023) are at the forefront of AI research and application, Gemini Pro 1.5 introduces several key advancements that differentiate it from Gemini Ultra. The table below provides an overview and comparison of both models. Use Cases  Analyzing Lengthy Videos Analyzing videos is another great capability brought by the fact that Gemini models are naturally multimodal, and this becomes even more compelling with long contexts. In the technical report, Gemini 1.5 Pro was able to analyze movies, like Buster Keaton’s silent 45-minute “Sherlock Jr.” movie. Using one frame per second, the researchers turned the movie into an input context of 684k tokens.  The model can then answer fairly complex questions about the video content, such as: “Tell me some key information from the piece of paper that is removed from the person’s pocket and the timecode of that moment.” Or, a very cursory line drawing of something that happened, combined with “What is the timecode when this happens?” Gemini 1.5 analyzing and reasoning over the 45-minute “Sherlock Jr.” movie You can see this interaction here: Multimodal prompting with a 44-minute movie Navigating Large and Unfamiliar Codebases As another code-related example, imagine you’re unfamiliar with a large codebase and want the model to help you understand the code or find where a particular functionality is implemented. In another example, the model can ingest an entire 116-file JAX code base (746k tokens) and help users identify the specific spot in the code that implements the backward pass for auto differentiation. It’s easy to see how the long context capabilities can be invaluable when diving into an unfamiliar code base or working with one you use daily. According to a technical lead, many Gemini team members have been finding it very useful to use Gemini 1.5 Pro’s long context capabilities on our Gemini code base. Gemini 1.5 navigating large and unfamiliar codebases What’s Next? According to a Google blog post, Gemini 1.5 Pro is currently in private preview, and its general availability with a standard 128,000-token context window will come later. Developers and enterprise customers can sign up to try Gemini 1.5 Pro with a context window of up to an experimental 1 million tokens via AI Studio and Google Vertex AI to upload hundreds of pages of text, entire code repos, and long videos and let Gemini reason across them. Try Gemini 1.5 Pro with a context window of up to an experimental 1 million tokens via AI Studio and Google Vertex AI That’s all for now. In the meantime, check out our resources on multimodal AI: Introduction to Multimodal Deep Learning GPT-4 Vision Alternatives Top Multimodal Annotation Tools

Feb 17 2024

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Meta’s V-JEPA: Video Joint Embedding Predictive Architecture Explained

Following the launch of I-JEPA last year, Meta has now rolled out V-JEPA as they accelerate efforts to envision Yann LeCun’s vision for Advanced Machine Intelligence.  Yann LeCun, Vice President & Chief AI Scientist at Meta, asserts that "V-JEPA is a step toward a more grounded understanding of the world so machines can achieve more generalized reasoning and planning." This statement reiterates the broader goal of advancing machine intelligence to emulate human learning processes, where internal models of the world are constructed to facilitate learning, adaptation, and efficient planning in complex tasks. What is  V-JEPA? V-JEPA is a vision model that is exclusively trained using a feature prediction objective. In contrast to conventional machine learning methods that rely on pre-trained image encoders, text, or human annotations, V-JEPA learns directly from video data without the need for external supervision. Key Features of V-JEPA  Self-supervised Learning V-JEPA employs self-supervised learning techniques, enhancing its adaptability and versatility across various tasks without necessitating labeled data during the training phase. Feature Prediction Objective Instead of reconstructing images or relying on pixel-level predictions, V-JEPA prioritizes video feature prediction. This approach leads to more efficient training and superior performance in downstream tasks. Efficiency With V-JEPA, Meta has achieved significant efficiency gains, requiring shorter training schedules compared to traditional pixel prediction methods while maintaining high performance levels. Versatile Representations V-JEPA produces versatile visual representations that excel in both motion and appearance-based tasks, showcasing its effectiveness in capturing complex interactions within video data. V-JEPA Methodology Revisiting Feature Prediction for Learning Visual Representations from Video The AI model is trained using the VideoMix2M dataset, where it passively observes video pixels without explicit guidance. Through an unsupervised feature prediction objective, V-JEPA learns to predict features within the videos without relying on external labels or annotations, setting it apart from traditional approaches. The model does not utilize pretrained image encoders, text, negative examples, human annotations, or pixel-level reconstruction during its training process. Instead of directly decoding pixel-level information, V-JEPA makes predictions in latent space, distinguishing it from generative methods. A conditional diffusion model is then trained to decode these feature-space predictions into interpretable pixels, with the pre-trained V-JEPA encoder and predictor networks remaining frozen throughout this process. Importantly, the decoder is only provided with representations predicted for the missing regions of the video and does not access unmasked regions. This methodology ensures that the feature predictions made by V-JEPA exhibit spatio-temporal consistency with the unmasked regions of the video, contributing to its ability to produce versatile visual representations that perform well on downstream video and image tasks without the need for adapting the model's parameters. Advantages over Pixel Prediction V-JEPA makes predictions in an abstract representation space, allowing it to focus on higher-level conceptual information in videos without getting bogged down by irrelevant details. It's the first video model adept at "frozen evaluations," where pre-training on the encoder and predictor is done once and then left untouched. This means adapting the model for new tasks only requires training a lightweight specialized layer on top, making the process efficient and quick. Unlike previous methods that required full fine-tuning for each task, V-JEPA's approach enables reusing the same model parts for multiple tasks without the need for specialized training each time, demonstrating its versatility in tasks like action classification and object interactions. Revisiting Feature Prediction for Learning Visual Representations from Video V-JEPA Performance V-JEPA was trained on a vast dataset comprising 2 million videos sourced from public datasets. The model was then evaluated on a range of downstream image and video tasks, demonstrating impressive performance across the board. Comparison with Pixel Prediction V-JEPA was assessed against video approaches relying on pixel prediction, ensuring a consistent architecture across all baselines. Models such as VideoMAE, Hiera, and OmniMAE were evaluated using either a ViT-L/16 encoder or a Hiera-L encoder, which had similar parameters. The evaluation encompassed frozen evaluation with an attentive probe on downstream video and image tasks, as well as end-to-end fine-tuning. Revisiting Feature Prediction for Learning Visual Representations from Video V-JEPA exhibited superior performance across all downstream tasks in frozen evaluation, with the exception of ImageNet, where it achieved a comparable accuracy of 74.8% to the 75.1% attained by an OmniMAE model trained directly on ImageNet. Under the fine-tuning protocol, V-JEPA surpassed other models trained with a ViT-L/16, matching the performance of Hiera-L, while utilizing significantly fewer samples during pretraining, underscoring the efficiency of feature prediction as a learning principle. Comparison with State-of-the-Art models The performance of V-JEPA models, pre-trained on video, was compared against the largest state-of-the-art self-supervised image and video models. This comparison included various baselines, such as OpenCLIP, DINOv2, and I-JEPA for image-pretrained models, and VideoMAE, OmniMAE, Hiera, VideoMAEv2, and MVD for video-pretrained models.  Revisiting Feature Prediction for Learning Visual Representations from Video The evaluation involved frozen evaluation with an attentive probe on downstream image and video tasks, showing V-JEPA's consistent improvement across all tasks, particularly excelling in tasks requiring motion understanding. It effectively reduced the gap between video and image models on tasks requiring static appearance-based features. V-JEPA Use-cases Video Understanding V-JEPA excels in understanding the content of various video streams, making it invaluable for computer vision tasks such as video classification, action recognition, and spatio-temporal action detection. Its ability to capture detailed object interactions and distinguish fine-grained actions sets it apart in the field of video understanding. Contextual AI Assistance The contextual understanding provided by V-JEPA lays the groundwork for developing AI assistants with a deeper understanding of their surroundings. Whether it's providing context-aware recommendations or assisting users in navigating complex environments, V-JEPA can enhance the capabilities of AI assistants in diverse scenarios. Augmented Reality (AR) Experiences V-JEPA's contextual understanding of video content can enrich AR experiences by providing relevant contextual information overlaid on the user's surroundings. Whether it's enhancing gaming experiences or providing real-time information overlays, V-JEPA can contribute to the development of immersive AR applications. With the release of Apple's Vision Pro, this technology could play a crucial role in enhancing mixed reality experiences. JEPA for Advanced Machine Intelligence (AMI) The primary focus of V-JEPA's development has centered on perception—grasping the contents of various video streams to gain an immediate contextual understanding of the world around us. The predictor within the Joint Embedding Predictive Architecture serves as an early physical world model, capable of conceptualizing what's happening within a video frame without needing to analyze every detail. Looking ahead, Meta's aim is to leverage this predictive model for planning and sequential decision-making tasks, expanding its utility beyond mere perception. Read the paper by Yann LeCun A Path Towards Autonomous Machine Intelligence for more information.   As a research model, V-JEPA holds promise for various future applications. Its contextual understanding could prove invaluable for embodied AI endeavors and the development of contextual AI assistants for future augmented reality (AR) glasses. Emphasizing responsible open science, Meta has released the V-JEPA model under the CC BY-NC license, encouraging collaboration and further extension of this groundbreaking work in the AI research community. You can find V-JEPA’s open source code on Meta AI’s GitHub.  

Feb 16 2024

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OpenAI Releases New Text-to-Video Model, Sora

OpenAI has responded to the recent debut of Google's Lumiere, a space-time diffusion model for video generation, by unveiling its own creation: Sora. The diffusion model can transform short text descriptions into high-definition video clips up to one minute long. How Does Sora Work? Sora is a diffusion model that starts with a video that resembles static noise. Over many steps, the output gradually transforms by removing the noise. By providing the model with the foresight of multiple frames concurrently, OpenAI has resolved the complex issue of maintaining subject consistency, even when it momentarily disappears from view. OpenAI Sora - AI Video Output Similar to GPT models, Sora uses a transformer architecture. Images and videos are represented as patches, collections of smaller units of data. By representing the data in the same manner, OpenAI was able to train diffusion transformers on a wide range of data of different durations, resolutions, and aspect ratios.  Sora leverages the recaptioning techniques from DALL-E3 and as such, the model follows the user’s text instructions closely.  Technical overview of OpenAI’s Sora OpenAI has released a few technical details on how the state-of-the-art diffusion model for video generation. Here are the key methodologies and features employed in Sora’s architecture. Video Generated by OpenAI's Sora Unified Representation for Large-Scale Training Sora focuses on transforming visual data into a unified representation conducive to large-scale training of generative models. Unlike previous approaches that often concentrate on specific types of visual data or fixed-size videos, Sora embraces the variability inherent in real-world visual content. By training on videos and images of diverse durations, resolutions, and aspect ratios, Sora becomes a generalist model capable of generating high-quality videos and images spanning a wide range of characteristics. Patch-Based Representations Inspired by the use of tokens in large language models (LLMs), Sora adopts a patch-based representation of visual data. This approach effectively unifies diverse modalities of visual data, facilitating scalable and efficient training of generative models. Patches have demonstrated their effectiveness in modeling visual data, enabling Sora to handle diverse types of videos and images with ease. Turning Visual Data into Patches Video Compression Network To convert videos into patches, Sora first compresses the input videos into a lower-dimensional latent space, preserving both temporal and spatial information. This compression is facilitated by a specialized video compression network, which reduces the dimensionality of visual data while maintaining its essential features. The compressed representation is subsequently decomposed into spacetime patches, which serve as transformer tokens for Sora's diffusion transformer architecture. Diffusion Transformer Sora leverages a diffusion transformer architecture, demonstrating remarkable scalability as video models. Diffusion transformers have proven effective across various domains, including language modeling, computer vision, and image generation. Sora's diffusion transformer architecture enables it to effectively handle video generation tasks, with sample quality improving significantly as training compute increases. Scaling Transformers for Video Generation Native Size Training for High-Quality Video Generation Sora benefits from training on data at its native size, rather than resizing, cropping, or trimming videos to standardized dimensions. This approach offers several advantages, including sampling flexibility, improved framing and composition, and enhanced language understanding. By training on videos at their native aspect ratios, Sora achieves superior composition and framing, resulting in high-quality video generation. Language Understanding and Text-to-Video Generation Training Sora for text-to-video generation involves leveraging advanced language understanding techniques, including re-captioning and prompt generation using models like DALL·E and GPT. Highly descriptive video captions improve text fidelity and overall video quality, enabling Sora to generate high-quality videos accurately aligned with user prompts. Capabilities of Sora OpenAI’s Sora can generate intricate scenes encompassing numerous characters, distinct forms of motion, and precise delineations of subject and background. As OpenAI states “The model understands not only what the user has asked for in the prompt, but also how those things exist in the physical world.” Capabilities of OpenAI Sora Here is an extensive list of capabilities of Sora that OpenAI demonstrated. This definitely says a lot about how powerful it is as a text-to-video tool for creating content generation and simulation tasks. Prompting with Images and Videos Sora's flexibility extends to accepting inputs beyond text prompts, including pre-existing images or videos. Glimpse of Prompt Generated Artwork of an Art Gallery by OpenAI's Sora Animating DALL-E Images Sora can generate videos from static images produced by DALL·E, showcasing its ability to seamlessly animate still images and bring them to life through dynamic video sequences.  Current techniques for animating images utilize neural-based rendering methods to produce lifelike animations. However, despite these advancements, achieving precise and controllable image animation guided by text remains a challenge, especially for open-domain images taken in diverse real-world environments. Models like AnimateDiff, AnimateAnything, etc have also demonstrated promising results for animating static images. Extending Generated Videos Sora is adept at extending videos, whether forward or backward in time, to create seamless transitions or produce infinite loops. This capability enables Sora to generate videos with varying starting points while converging to a consistent ending, enhancing its utility in video editing tasks. Video-to-Video Editing Leveraging diffusion models like SDEdit, Sora enables zero-shot style and environment transformation of input videos, showcasing its capability to manipulate video content based on text prompts and editing techniques. Connecting Videos Sora facilitates gradual interpolation between two input videos, facilitating seamless transitions between videos with different subjects and scene compositions. This feature enhances Sora's ability to create cohesive video sequences with diverse visual content. Image Generation Sora is proficient in generating images by arranging patches of Gaussian noise in spatial grids with a temporal extent of one frame, offering flexibility in generating images of variable sizes up to 2048 x 2048 resolution. Photorealistic Image Generation Capability of OpenAI Sora Simulation Capabilities At scale, Sora exhibits amazing simulation capabilities, enabling it to simulate aspects of people, animals, environments, and digital worlds without explicit inductive biases. These capabilities include: 3D Consistency: Generating videos with dynamic camera motion, ensuring consistent movement of people and scene elements through three-dimensional space. Long-Range Coherence and Object Permanence: Effectively modeling short- and long-range dependencies, maintaining temporal consistency even when objects are occluded or leave the frame. Interacting with the World: Simulating actions that affect the state of the world, such as leaving strokes on a canvas or eating a burger with persistent bite marks. Simulating Digital Worlds: Simulating artificial processes, including controlling players in video games like Minecraft while rendering high-fidelity worlds and dynamics. Limitations of Sora Limitation of OpenAI's Sora - Glass Shattering Effect OpenAI acknowledged that the current AI model has known weaknesses, including: Struggling to accurately simulate complex space Understand some instances of cause and effect Confuse spatial details of a prompt Precise descriptions of events over time Safety Considerations of Sora OpenAI is currently working with a team of red teamers to test the AI model prior to making Sora available to OpenAI users. These red teamers consist of domain experts familiar with misinformation, hateful content, and bias.  In their release, OpenAI has stated that they will not only leverage existing safety methods leveraged for the release of DALL-E3 but also going one step further to build tools to detect misleading content, including a detection classifier that can identify a video generated by Sora. Once the model is released in OpenAI’s products, they will include C2PA metadata and be monitored by their text and image classifiers: input prompts that violate their usage policy will be rejected and video outputs will be reviewed frame by frame.  In addition to all these safety precautions, OpenAI has also stated they will engage policymakers, educators, and artists to understand concerns and identify use cases for the model.  Text-to-video synthesis with Sora Noteworthy Text to Video Generation Models Google’s Lumiere Google’s recent introduction of its text-to-video diffusion model, Lumiere is truly remarkable as well. It is designed to generate realistic, diverse, and coherent motion in videos. Lumiere’s capabilities include: text-to-video generation image-to-video generation stylized generation text-based video editing animating content of an image within a user-provided region Video inpainting Unlike traditional approaches that rely on cascaded designs involving distant keyframe generation and subsequent temporal super-resolution, Lumiere introduces Space-Time I-Net architecture. This architecture allows Lumiere to generate the entire temporal duration of the video at once, streamlining the synthesis process and improving global temporal consistency. Google Lumiere's Prompt Generated AI Video By incorporating spatial and temporal down- and up-sampling techniques and leveraging pre-trained text-to-image diffusion models, Lumiere achieves remarkable results in generating full-frame-rate, low-resolution videos. This approach not only enhances the overall visual quality of the synthesized videos but also facilitates a wide range of content creation tasks and video editing applications, including image-to-video conversion, video inpainting, and stylized generation. For more information, read the paper Lumiere: A Space-Time Diffusion Model for Video Generation.   Stability AI’s Stable Video Diffusion Stability AI introduced Stable Video Diffusion, a latent video diffusion model designed for state-of-the-art text-to-video and image-to-video generation tasks. Leveraging recent advancements in latent diffusion models (LDMs) initially trained for 2D image synthesis, Stability AI extends its capabilities to generate high-resolution videos by incorporating temporal layers and fine-tuning them on specialized video datasets. Stable Video Diffusion Stability AI addresses the lack of standardized training methods by proposing and evaluating three key stages for successful training of video LDMs: text-to-image pretraining, video pretraining, and high-quality video finetuning. Emphasizing the importance of a meticulously curated pretraining dataset for achieving high-quality video synthesis, Stability AI presents a systematic curation process, including strategies for captioning and data filtering, to train a robust base model. The Stable Video Diffusion model demonstrates the effectiveness of finetuning the base model on high-quality data, resulting in a text-to-video model that competes favorably with closed-source video generation methods. The base model not only provides a powerful motion representation for downstream tasks such as image-to-video generation but also exhibits adaptability to camera motion-specific LoRA modules. It also showcases the versatility of its model by demonstrating its strong multi-view 3D-prior capabilities, serving as a foundation for fine-tuning a multi-view diffusion model that generates multiple views of objects in a feedforward manner. This approach outperforms image-based methods while requiring a fraction of their compute budget, highlighting the efficiency and effectiveness of Stable Video Diffusion in generating high-quality videos across various applications. For more information, read the paper Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets. Meta’s Make-A-Video Meta two years ago introduced Make-A-Video. Make-A-Video leverages paired text-image data to learn representations of the visual world and utilize unsupervised learning on unpaired video data to capture realistic motion. This innovative approach offers several advantages: It expedites the training of text-to-video models by leveraging pre-existing visual and multimodal representations It eliminates the need for paired text-video data It inherits the vast diversity of aesthetic and fantastical depictions from state-of-the-art image generation models. Meta's Make-A-Video Generated Graphic Make-A-Video is a simple yet effective architecture that builds on text-to-image models with novel spatial-temporal modules. First, full temporal U-Net and attention tensors are decomposed and approximated in space and time. Then, a spatial-temporal pipeline is designed to generate high-resolution and frame-rate videos, incorporating a video decoder, interpolation model, and two super-resolution models to enable various applications beyond text-to-video synthesis. Despite the limitations of text describing images, Make-A-Video demonstrates surprising effectiveness in generating short videos. By extending spatial layers to include temporal information and incorporating new attention modules, Make-A-Video accelerates the T2V training process and enhances visual quality. Sora: Key Highlights With a SOTA diffusion model, Sora empowers users to effortlessly transform text descriptions into captivating high-definition video clips, revolutionizing the way we bring ideas to life. Here are the key highlights of Sora: Sora's Architecture: Utilizes a diffusion model and transformer architecture for efficient training. Sora's Methodologies: Sora uses methodologies like unified representation, patch-based representations, video compression network, and diffusion transformer. Capabilities: Includes image and video prompting, DALL·E image animation, video extension, editing, image generation, etc. Limitations: Weaknesses in simulating complex space and understanding causality. Sora's Safety Considerations: Emphasizes safety measures like red team testing, content detection, and engagement with stakeholders. Other significant text-to-video models: Lumiere, Stable Video Diffusion, and Make-A-Video.

Feb 15 2024

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A Guide to Machine Learning Model Observability

Artificial intelligence (AI) is solving some of society's most critical issues. But, lack of transparency and visibility in building models make AI a black box. Users cannot understand what goes on behind the scenes when AI answers a question, gives a prediction, or makes a critical decision. What happens when large language models (LLMs), like GPT-4 and LLaMA, make errors, but users fail to realize the mistake? Or how badly is the model’s credibility affected when users identify errors in the model outcome?  Customers lose trust, and the company behind the model can face serious legal and financial consequences. Remember how a minor factual error in Bard’s demo reportedly cost Google $100 billion in market value?  That’s where model observability comes into play. It helps understand how a model reaches an outcome using different techniques. In this article, you will: Understand model observability and the importance Challenges related to model observability How model observability applies to modern AI domains, like computer vision and natural language processing (NLP)  What is Model Observability? Model observability is a practice to validate and monitor machine learning (ML) model performance and behavior by measuring critical metrics, indicators, and processes to ensure that the model works as expected in production.  It involves end-to-end event logging and tracing to track and diagnose issues quickly during training, inference, and decision-making cycles. It lets you monitor and validate training data to check if it meets the required quality standards. It also assists in model profiling, detecting bias and anomalies that could affect the ML model's performance Through observability, ML engineers can conduct root-cause analysis to understand the reason behind a particular issue. This practice allows for continuous performance improvement using a streamlined ML workflow that enables scalability and reduces time to resolution. A significant component of observability is model explainability, which operates under explainable AI or XAI. XAI facilitates root-cause analysis by enabling you to investigate a model’s decision-making process. XAI optimizes model development, debugging, and testing cycles using tools and frameworks. ML model observability is often used interchangeably with ML monitoring. However, an emphasis on model explainability and a focus on why a specific issue occurred makes ML observability broader than ML monitoring. While model monitoring only tells you where and what the problem is, observability goes further by helping you understand the primary reason behind a particular situation. Learn more about the difference between model monitoring and model observability by reading our detailed article ML Observability vs. ML Monitoring   Significance of Model Observability ML models in production can encounter several issues that can cause performance degradation and result in poor user experience. These issues can go unnoticed if model observability practices are not followed. With a robust model observability pipeline, data scientists can prevent such problems and speed up the development lifecycle. Below are a few factors that can go wrong during production and warrant the need for model observability. Data Drift Data drift occurs when the statistical properties of a machine learning model’s training data change. It can be a covariate shift where input feature distributions change or a model drift where the relationship between the input and target variables becomes invalid. The divergence between the real-world and training data distribution can occur for multiple reasons, such as changes in underlying customer behavior, changes in the external environment, demographic shifts, product upgrades, etc. Data Drift Performance Degradation As the machine learning application attracts more users, model performance can deteriorate over time due to model overfitting, outliers, adversarial attacks, and changing data patterns. Data Quality Ensuring consistent data quality during production is challenging as it relies heavily on data collection methods, pipelines, storage platforms, pre-processing techniques, etc. Problems such as missing data, labeling errors, disparate data sources, privacy restraints, inconsistent formatting, and lack of representativeness can severely damage data quality and cause significant prediction errors. Let’s discuss how model observability helps. Faster Detection and Resolution of Issues Model observability solutions track various data and performance metrics in real-time to quickly notify teams if particular metrics breach thresholds and enhance model interpretability to fix the root cause of the problem. Regulatory Compliance  Since observability tools maintain logs of model behavior, datasets, performance, predictions, etc., they help with compliance audits and ensure that the model development process aligns with regulatory guidelines. Moreover, they help detect bias by making the model decision-making process transparent through XAI. Fostering Customer Trust Model observability enables you to build unbiased models with consistent behavior and reduces major model failures. Customers begin to trust applications based on these models and become more willing to provide honest feedback, allowing for further optimization opportunities. Now, let’s discuss how model observability improves the ML pipeline for different domains. Model Observability in Large Language Models (LLMs) and Computer Vision (CV) Standard machine learning observability involves model validation, monitoring, root-cause analysis, and improvement. During validation, an observability platform evaluates model performance on unseen test datasets and assesses whether a model suffers from bias or variance. It helps you detect issues during production through automated metrics. Finally, insights from the root-cause analysis help you improve the model and prevent similar problems from occurring again. While the flow above applies to all ML models, monitoring and evaluation methods can differ according to model complexity. For instance, LLMs and CV models process unstructured data and have complex inference procedures. So, model observability requires advanced explainability, evaluation, and monitoring techniques to ensure that the models perform as expected. Let’s discuss the main challenges of such models and understand the critical components required to make model observability effective in these domains. Model Observability in Large Language Models LLMs can suffer from unique issues, as discussed below. Hallucinations: Occurs when LLMs generate non-sensical or inaccurate responses to user prompts. No single ground truth: A significant challenge in evaluating an LLM's response is the absence of a single ground truth. LLMs can generate multiple plausible answers, and assessing which is accurate is problematic. Response quality: While responses may be factually accurate, they may not be relevant to a user’s prompt. Also, the language may be ambiguous with an inappropriate tone. The challenge here is to monitor response and prompt quality together, as a poorly crafted prompt will generate sub-optimal responses. Jailbreaks: Specific prompts can cause LLMs to disregard security protocols and generate harmful, biased, and offensive responses. Cost of retraining: Retraining LLMs is necessary to ensure that they generate relevant responses using the latest information. However, retraining is costly. It demands a robust infrastructure involving advanced hardware, personnel expenses, data management costs, etc. You can mitigate the above challenges using a tailored model observability strategy that will allow for better evaluation techniques. Below are common techniques to evaluate LLMs. User feedback: Users can quickly identify and report problems, such as bias, misinformation, and unethical LLM responses to specific prompts. Collecting and assessing user feedback can help improve response quality.  Embedding visualization: Comparing the model response and input prompt embeddings in a semantic space can reveal how close the responses to particular prompts are. The method can help evaluate response relevance and accuracy. A Word Embedding Plot Showcasing Relevance of Similar Words Prompt engineering: You can enhance LLM performance by investigating how it responds to several prompt types. It can also help reveal jailbreaking prompts early in the development process. Retrieval systems: A retrieval system can help you assess whether an LLM fetches the correct information from relevant sources. You can experiment by feeding different data sources into the model and trying various user queries to see if the LLM retrieves relevant content. Fine-tuning: Instead of re-training the entire LLM from scratch, you can fine-tune the model on domain-specific input data. Model Observability in Computer Vision Similar to LLMs, CV models have specific issues that require adapting model observability techniques for effectiveness. Below are a few problems with CV modeling. Image drift: Image data drift occurs when image properties change over time. For instance, certain images may have poor lighting, different background environments, different camera angles, etc. Occlusion: Occlusion happens when another object blocks or hides an image's primary object of interest. It causes object detection models to classify objects wrongly and reduces model performance. Occlusion Lack of annotated samples: CV models often require labeled images for training. However, finding sufficient domain-specific images with correct labels is challenging. Labeling platforms like Encord can help you mitigate these issues. Sensitive use cases: CV models usually operate in safety-critical applications like medical diagnosis and self-driving cars. Minor errors can lead to disastrous consequences. As such, model observability in CV must have the following components to address the problems mentioned above. Monitoring metrics: Use appropriate metrics to measure image quality and model performance. Specialized workforce: Domain experts must be a part of the model observability pipeline to help annotators with the image labeling process. Quality of edge devices: Image data collection through edge devices, such as remote cameras, drones, sensors, etc., requires real-time monitoring methods to track a device’s health, bandwidth, latency, etc. Label quality: Ensuring high label quality is essential for effective model training. Automation in the labeling process with a regular review system can help achieve quality standards. Smart labeling techniques, such as active, zero-shot, and few-shot learning, should be part of the observability framework to mitigate annotation challenges. Domain adaptation: CV observability should help indicate when fine-tuning a CV model is suitable by measuring the divergence between source and target data distribution. It should also help inform the appropriate adaptation technique. Want to learn how active learning can solve your CV data annotation challenges? Read our detailed article A Practical Guide to Active Learning for Computer Vision. Now, let’s explore the two crucial aspects of model observability: monitoring and explainability. We’ll discuss various techniques that you can employ in your ML pipelines. Model Monitoring Techniques The following model metrics are commonly used for evaluating the performance of standard ML systems, LLMs, and CV models. Standard ML monitoring metrics: Precision, recall, F1 score, AUC ROC, and Mean Absolute Error (MAE) are common techniques for assessing model performance by evaluating true positives and negatives against false positives and negatives. LLM monitoring metrics: Automated scores like BLEU, ROUGE, METEOR, and CiDER help measure the model response quality by matching n-grams in candidate and target texts. Moreover, human-level monitoring is preferable in generative AI domains due to the absence of a single ground truth. User feedback, custom metrics, and manually evaluating response relevance are a few ways to conduct human-based assessments. You can also implement reinforcement learning with human feedback (RLHF) to ensure LLM output aligns with human preferences. CV monitoring metrics: Metrics for monitoring CV models include mean average precision (mAP), intersection-over-union (IoU), panoptic quality (PQ), etc., for various tasks, such as object detection, image classification, and segmentation. Model Explainability Techniques The primary difference between ML observability and monitoring is that the former encompasses a variety of explainability techniques (XAI) to help you understand the fundamental reason behind an anomaly or error. XAI techniques can be classified into three levels of explainability:  Global,  Cohort,  Local.  Global explainability tells you which feature has the most significant contribution across all predictions on average. Global explainability helps people without a data science domain to interpret the model reasoning process.  For instance, the diagram below shows a model for predicting credit limits for individuals relies heavily on age. This behavior can be problematic since the model will always predict a lower credit limit for younger individuals, despite their ability to pay more. Global Explainability for a Model’s Features Similarly, cohort explainability techniques reveal which features are essential for predicting outputs on a validation or test dataset. A model may assign importance to one feature in the training phase but use another during validation. You can use cohort explainability to identify these differences and understand model behavior more deeply. The illustration below suggests the model uses “charges” as the primary feature for predicting credit limits for people under 30. Finally, local explainability methods allow you to see which feature the model used for making a particular prediction in a specific context during production. For instance, the credit limit model may predict a lower credit limit for someone over 50. Analyzing this case may reveal that the model used account balance as the primary predictor instead of age. Such a level of detail can help diagnose an issue more efficiently and indicate the necessary improvements. Explainability Techniques in Standard ML Systems Let’s briefly discuss the two techniques you can use to interpret a model’s decision-making process.  SHAP: Shapley Additive Explanations (SHAP) computes the Shapley value of each feature, indicating feature importance for global and local explainability. LIME: Local Interpretable Model-Agnostic Explanations (LIME) perturbs input data to generate fake predictions. It then trains a simpler model on the generated values to measure feature importance. Explainability Techniques in LLMs LLM explainability is challenging since the models have complex deep-learning architectures with many parameters. However, the explainability techniques mentioned below can be helpful. Attention-based techniques: Modern LLMs, such as ChatGPT, BERT, T5, etc., use the transformer architecture, which relies on the attention mechanism to perform NLP tasks. The attention-based explainability technique considers multiple words and relates them to their context for predictions. With attention visualization, you can see which words in an input sequence the model considered the most for predictions. Saliency-based techniques: Saliency methods consist of computing output gradients with respect to input features for measuring importance. The intuition is that features with high gradient values are essential for predictions. Similarly, you can erase or mask a particular input feature and analyze output variation. High variations mean that the feature is important. Explainability Techniques in CV While saliency-based methods are applicable for explaining CV model predictions, other methods specific to CV tasks can do a better job, such as: Integrated gradients The idea behind integrated gradients (IG) is to build a baseline image that contains no relevant information about an object. For instance, a baseline image can only have random noise. You can gradually add features, or pixels to the image and compute gradients at each stage. Adding up the gradients for each feature along the path will reveal the most important features for predicting the object within the image. IG Showing Which Pixels Are Important For Predicting the Image of a Cockatoo XRAI XRAIenhances the IG approach by highlighting pixel regions instead of single pixels by segmenting similar image patches and then computing the saliency of each region through gradients. XRAI Showing the Important Regions for Prediction Grad-CAM Gradient-weighted Class Activation Mapping (Grad-CAM) generates a heatmap for models based on Convolutional Neural Nets (CNNs). The heatmap highlights the regions of importance by overlaying it on the original image. Grad-CAM’s Heatmap Model Observability with Encord Active Encord Active is an evaluation platform for computer vision models. It helps you understand and improve your data, labels, and models at all stages of your computer vision journey. Here are some observability workflows you can use Encord Active to execute: Understanding Model Failure Using Detailed Explainability Reports Within Encord Active, you can visualize the impact of data and label quality metrics on your model's performance. This can also visualize Precision or Recall by Metric based on the model results to know which instances it needs to predict better. Let’s see how you can achieve this step-by-step within Encord Active. Make sure you have a project selected and have successfully imported model predictions (through an API or the UI).   Step 1: Navigate to the Model Evaluation tab. Step 2: Toggle the Metric Performance button. You will see a graph of your model’s performance based on the precision by the quality metric you select to identify model failure modes. Debug Data Annotation Errors When developing ML systems or running them in production, one key issue to look out for is the quality of your annotations. They can make or break the ML systems because they are only as good as the ground truth they are trained on. Here’s how you could do it within Active Cloud: Step 1: Navigate to Explorer and Click Predictions Step 2: Click Add Filter >> Annotation Quality Metrics, and select a metric you want to inspect. Perform Root-Cause Analysis Encord Active provides a quick method to filter unwanted or problematic images by auto-discovering the issues in your model predictions. Here’s how to do it: Ensure you are on the Explorer tab. Click on Predictions and on the right hand side of your scream, you should see model prediction issues Active has surfaced including the types. Those are shortcuts to improving datasets and model performance. They give you a quick launch pad to improve your data and model performance. With Encord Active, you can also: Use automated CV pipelines to build robust models. Use pre-built or custom quality metrics to assess CV model performance. Run robustness checks on your model before and after deployment. Challenges of Model Observability Despite effective tools, implementing an end-to-end model observability pipeline takes time and effort. The list below highlights the most pressing issues you must address before building an efficient observability workflow. Increasing model complexity: Modern AI models are highly complex, with many integrated modules, deep neural nets, pre-processing layers, etc. Validating, monitoring, and performing root-cause analysis on these sophisticated architectures is tedious and time-consuming. XAI in multimodal models: Explaining models that use multiple modalities - text, image, sound, etc. - is difficult. For instance, using salience-based methods to explain vision-language models that combine NLP and visual information for inferencing would be incredibly costly due to the sheer number of parameters. Privacy concerns: Privacy restrictions mean you cannot freely analyze model performance on sensitive data, which may lead to compliance issues. Scalability: Observing models that receive considerable inference requests requires a robust infrastructure to manage large data volumes. Human understanding and bias: This challenge concerns XAI, where explanations can differ based on the model used. Also, understanding explainability results requires background knowledge that teams outside of data science may not have. Future Trends in Model Observability While the above challenges make optimal model observability difficult to achieve, research in the direction of XAI shows promising future trends. For instance, there is considerable focus on making XAI more user-friendly by building techniques that generate simple explanations. Also, research into AI model fairness is ongoing, emphasizing using XAI to visualize learned features and employing other methods to detect bias. In addition, studies that combine insights from other disciplines, such as psychology and philosophy, are attempting to find more human-centric explainability methods. Finally, causal AI is a novel research area that aims to find techniques to highlight why a model uses a particular feature for predictions. This method can add value to explanations and increase model robustness. Model Observability: Key Takeaways As AI becomes more complex, observability will be crucial for optimizing model performance. Below are a few critical points you must remember about model observability. Observability for optimality: Model observability is essential for optimizing modern AI models through efficient validation, monitoring, and root-cause analysis. Better productivity: Observability improves resource allocations, reduces the cost of re-training, builds customer trust, ensures regulatory compliance, and speeds up error detection and resolution. LLM and CV observability: Observability for LLMs and CV models differs slightly from standard ML observability due to issues specific to NLP and CV tasks. Monitoring and explainability: Model monitoring and explainability are two primary components of observability, each with different techniques. Challenges and future trends: Model complexity is the primary issue with observability and will require better explainability techniques to help users understand how modern models think.

Jan 19 2024

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Data-Centric AI: Implement a Data Centered Approach to Your ML Pipeline

In the rapidly evolving landscape of artificial intelligence (AI), the emphasis on data-centric approaches has become increasingly crucial. As organizations strive to develop more robust and effective deep learning models, the spotlight is shifting toward understanding and optimizing the data that fuels these systems. In this blog post, we will explore the concept of data-centric AI, as coined by Andrew Ng. We will compare it with model-centric AI, delving into its significance and discussing the key principles, benefits, and challenges associated with this approach. We've distilled the essence of data-centric AI into a comprehensive whitepaper, now available for free download. Dive deeper into the practical aspects, unlocking secrets to supercharge your project. Adopt the data-centric approach to your AI project and unlock the potential within your project by signing up and downloading our whitepaper. ⚡️ What is Data Centric AI? Data-centric AI is an approach that places primary importance on the quality, diversity, and relevance of the data used to train and validate ML models. In contrast to the model-centric approach, which primarily focuses on optimizing the model architecture and hyperparameters, data-centric AI acknowledges that the quality of the data is often the decisive factor in determining the success of an AI system. Overview Model-Centric AI Concentrates on refining the architecture, hyperparameters, and optimization techniques of the ML model. Assumes that a well-optimized model will inherently adapt to various scenarios without requiring frequent adjustments to the training data. Risks may arise if the model is not capable of handling real-world variations or if the training data needs to adequately represent the complexities of the application domain. Data-Centric AI Prioritizes the quality, diversity, and relevance of the training data, acknowledging that the model's success is heavily dependent on the data it learns from. Enables models to adapt to evolving data distributions, dynamic environments, and changing real-world conditions, as the focus is on the data's ability to represent the complexities of the application domain. Mitigates risks associated with biased predictions, unreliable generalization, and poor model performance by ensuring better data. Importance of Data-Centric AI As we've observed, in today's data-driven landscape your machine learning models (ML models) are directly influenced by the quality of your data. While the quantity of data is important, superior data quality takes precedence. High-quality data ensures more accurate insights, reliable predictions, and ultimately, greater success in achieving the objectives of your AI project. Here are some of the key benefits of data-centric AI: Improved Model Performance: Adopting a data-centric approach enhances AI model adaptability to evolving real-world conditions, thriving in dynamic environments through relevant and up-to-date training data.  Enhanced Generalization: Models trained on representative data generalize better to unseen scenarios. For instance, active learning, a data-centric approach, strategically labels informative data points, improving model efficiency and enhancing generalization by learning from the most relevant examples. Improved Explainability: It prioritizes model interpretability, fostering transparency by making models observable. Continuous Cycle of Improvement: Data-centric AI initiates a continuous improvement cycle, leveraging feedback from deployed models to refine data and models. Challenges of Data-Centric AI While data-centric AI offers significant advantages, it also presents some challenges: Data Quality Assurance: Ensuring the quality and accuracy of data can be challenging, especially in dynamic environments. Requires a Shift in Mindset: Moving from a model-centric to a data-centric approach requires a cultural shift within organizations. Lack of Research: Within the community, few AI researchers are working on establishing standardized frameworks for effective implementation and optimization of data-centric AI strategies compared to model-centric approaches as this concept is relatively new. Discover effective strategies for overcoming challenges in adopting a data-centric AI approach. Read our whitepaper, 'How to Adopt a Data-Centric AI,' and unlock insights into actionable steps to address these obstacles for free.  Key Principles of Data-Centricity Now, let's dive into the fundamental principles that underpin a successful data-centric AI approach, guiding organizations in overcoming challenges and optimizing their data-centric strategies. Data Quality and Data Governance To lay a sturdy foundation, organizations must prioritize data quality and implement robust data governance practices. This involves ensuring that the data used is of high quality, accurate, consistent, and reliable. Establishing governance frameworks helps maintain data integrity, traceability, and accountability throughout its lifecycle. Data Curation, Storage, and Management Effective data curation, secure storage, and efficient management are essential components of a data-centric strategy. Organizations should focus on curating data thoughtfully, optimizing storage for accessibility, and implementing efficient data management practices. This ensures streamlined access to data while preserving its integrity, and supporting effective decision-making processes. Data Security and Privacy Measures As the value of data increases so does the importance of robust security and privacy measures. Organizations need to implement stringent protocols to safeguard sensitive information. This includes encryption, access controls, and compliance with privacy regulations. By prioritizing data security and privacy, organizations can build trust with stakeholders and ensure responsible data handling. Establishing a Data-Driven Organizational Culture Fostering a data-driven culture is vital for data-centric AI success. Cultivate an environment where stakeholders value data, promoting collaboration, innovation, and decision-making based on quality insights. This cultural shift transforms data into a strategic asset, driving organizational growth and success. Now that we've laid the groundwork with the core principles of successful data-centric AI, it's time to roll up your sleeves and get into the real action. But how do you translate these principles into a concrete, step-by-step implementation plan? That's where our exclusive white paper, "How to Adopt a Data-Centric AI", comes in. 🚀    Overview of Data-Centric Approach Let's break down the key steps in this data-driven approach: Data Collection and Data Management This section prioritizes the methodology of gathering datasets from a variety of data sources like open source datasets. The data science teams often streamline this process setting the stage for subsequent stages of the AI project.  Data Cleaning, Data Augmentation, and Data Preprocessing During this phase, the focus shifts to refining and ensuring the quality of collected data. Techniques such as using synthetic data or augmentation techniques enrich the dataset, mitigating biases, enhancing generalization, and preventing overfitting. This process can be optimized through the utilization of data platforms like Encord, enabling efficient data analysis and processing to ensure data integrity and expedite the preparation of high-quality datasets. Discover the steps to transform your project into a data-centric approach by reading the whitepaper How to Adopt Data-Centric AI.    Data Labeling and Feature Engineering Data annotation takes center stage in defining the overall data quality for the project. Organizations meticulously label instances, providing ground truth for training AI models. Whether categorizing images, transcribing text, or labeling objects, this step empowers ML models, contributing to accuracy and reliability. The next steps include feature engineering and selection, and enhancing input data quality. Feature engineering demands domain knowledge to craft meaningful feature subsets, while selection identifies the most relevant attributes, ensuring ML models possess accurate information for precise predictions. Model Training, Continuous Monitoring, and Data Feedback Model training is the phase where AI models learn from the prepared data, while validation is equally crucial to ensuring that trained models meet the desired accuracy and performance benchmarks. Additionally, the iterative process of finetuning further refines models enhancing their effectiveness based on real-world performance feedback. Beyond deployment, data feedback detects deviations, utilizing real-world insights to iteratively refine neural networks and data strategies, ensuring continuous evolution and relevance in the dynamic landscape of data-centric AI. Unlock the power of data-centric AI with our whitepaper! Dive into key steps like data curation, cleaning, labeling, and more. 🚀   Remember, data-centric AI is an iterative process, not a linear one. By embracing this continuous cycle of improvement, you'll unlock the true potential of your data, propelling your ML algorithms to new advancements. Data-Centric AI: Key Takeaways Shifting Focus: Move beyond model-centric approaches and prioritize the quality, diversity, and relevance of your data for building robust and effective AI systems. Data-Centric Advantages: Achieve improved model performance, enhanced generalization, better explainability, and continuous improvement through data-driven strategies. Challenges and Solutions: Address data quality assurance, cultural shifts, limited research, and security concerns by implementing data governance, efficient management, robust security measures, and a data-driven organizational culture. Implementation Steps: Implement a data-centric approach through data collection, cleaning, augmentation, preprocessing, labeling, feature engineering, selection, model training, finetuning, continuous monitoring, and data feedback. Iterative Cycle: Embrace a continuous iteration of improvement by using data feedback to refine your data and models, unlocking the true potential of data-centric AI. Remember: Data is the fuel for your AI engine. Prioritize its quality and unleash the power of data-driven solutions for success in the evolving landscape of AI and ML. Read our brand new whitepaper to understand How to Adopt a Data-Centric Approach to AI!  

Jan 11 2024

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Google Launches Gemini, Its New Multimodal AI Model

For the past year, an artificial intelligence (AI) war has been going on between the tech giants OpenAI, Microsoft, Meta, Google Research, and others to build a multimodal AI system.  Alphabet and Google’s CEO Sundar Pichai has teamed up with DeepMind’s CEO Demis Hassabis has launch the much-anticipated generative AI system Gemini, their most capable and general artificial intelligence (AI) natively multimodal model—meaning it comprehends and generates texts, audio, code, video, and images. It outperforms OpenAI’s GPT-4 in general tasks, reasoning capabilities, math, and code. This launch follows Google’s very own LLM, PaLM 2 released in April, some of the family of models powering Google’s search engine. What is Google Gemini? The inaugural release, Gemini 1.0, represents a pinnacle in artificial intelligence, showcasing remarkable versatility and advancement. This generative AI model is well-equipped for tasks demanding the integration of multiple data types, designed with a high degree of flexibility and scalability to operate seamlessly across diverse platforms, ranging from expansive data centers to portable mobile devices. The models demonstrate exceptional performance, exceeding current state-of-the-art results in numerous benchmarks. It is capable of sophisticated reasoning and problem-solving, even outperforming human experts in some scenarios. Now, let's dive into the technical breakthroughs that underpin Gemini's extraordinary capabilities. Proficiency in Handling -  Text, Video, Code, Image, and Audio Gemini 1.0 is designed with native multimodal capabilities, as they are trained jointly across text, image, audio, and video. The joint training on diverse data types allows the AI model to seamlessly comprehend and generate content across diverse data types. It exhibits exceptional proficiency in handling: Text Gemini's prowess extends to advanced language understanding, reasoning, synthesis, and problem-solving in textual information. Its proficiency in text-based tasks positions it among the top-performing large language models (LLMs), outperforming inference-optimized models like GPT-3.5 and rivaling some of the most capable models like PaLM 2, Claude 2, etc. Google Gemini: A Family of Highly Capable Multimodal Models Gemini Ultra excels in coding, a popular use case of current Large Language Models (LLMs). Through extensive evaluation of both conventional and internal benchmarks, Gemini Ultra showcases its prowess in various coding-related tasks. In the HumanEval standard code-completion benchmark, where the model maps function descriptions to Python implementations, instruction-tuned Gemini Ultra correctly implements an impressive 74.4% of problems. Moreover, on a newly introduced held-out evaluation benchmark for Python code generation tasks, Natural2Code, ensuring no web leakage, Gemini Ultra attains the highest score of 74.9%. These results underscore Gemini's exceptional competence in coding scenarios, positioning it at the forefront of AI models in this domain. Multimodal reasoning capabilities applied to code generation. Gemini Ultra showcases complex image understanding, code generation, and instructions following. Image  Gemini performs comparable to OpenAI’s GPT-4V or prior SOTA models in image understanding and generation. Gemini Ultra consistently outperforms the existing approaches even in zero-shot, especially for OCR-related image understanding tasks without any external OCR engine. It achieves strong performance across a diverse set of tasks, such as answering questions on natural images and scanned documents, as well as understanding infographics, charts, and science diagrams. Gemini can output images natively without having to rely on an intermediate natural language description that can bottleneck the model’s ability to express images. This uniquely enables the model to generate images with prompts using the interleaved image and text sequences in a few-shot setting. For example, the user might prompt the model to design suggestions of images and text for a blog post or a website. Google Gemini: A Family of Highly Capable Multimodal Models Video Understanding Gemini's capacity for video understanding is rigorously evaluated across held-out benchmarks. Sampling 16 frames per video task, Gemini models demonstrate exceptional temporal reasoning. In November 2023, Gemini Ultra achieved state-of-the-art results on few-shot video captioning and zero-shot video question-answering tasks, confirming its robust performance.  Google Gemini: A Family of Highly Capable Multimodal Models The example above provides a qualitative example, showcasing Gemini Ultra's ability to comprehend ball-striking mechanics in a soccer player's video, demonstrating its prowess in enhancing game-related reasoning. These findings establish Gemini's advanced video understanding capabilities, a pivotal step in crafting a sophisticated and adept generalist agent. Audio Understanding Gemini Nano-1 and Gemini Pro’s performance is evaluated for tasks like automated speed recognition (ASR) and automated speech translation (AST). The Gemini models are compared against the Universal Speech Model (USM) and Whisper across diverse benchmarks.  Google Gemini: A Family of Highly Capable Multimodal Models Gemini Pro stands out significantly, surpassing USM and Whisper models across all ASR and AST tasks for both English and multilingual test sets. The FLEURS benchmark, in particular, reveals a substantial gain due to Gemini Pro's training with the FLEURS dataset, outperforming its counterparts. Even without FLEURS, Gemini Pro still outperforms Whisper with a WER of 15.8. Gemini Nano-1 also outperforms USM and Whisper on all datasets except FLEURS. While Gemini Ultra's audio performance is yet to be evaluated, expectations are high for enhanced results due to its increased model scale.  Triads of Gemini Model The model comes in three sizes, with each size specifically tailored to address different computational limitations and application requirements: Gemini Ultra The Gemini architecture enables efficient scalability on TPU accelerators, empowering the most capable model, the Gemini Ultra, to achieve state-of-the-art performance across diverse and complex tasks, including reasoning and multimodal functions. Gemini Pro An optimized model prioritizing performance, cost, and latency, excelling across diverse tasks. It demonstrates robust reasoning abilities and extensive multimodal capabilities. Gemini Nano Gemini Nano is the most efficient mode and is designed to run on-device. It comes in two versions: Nano-1 with 1.8B parameters for low-memory devices and Nano-2 with 3.25B parameters for high-memory devices. Distilled from larger Gemini models, it undergoes 4-bit quantization for optimal deployment, delivering best-in-class performance. Now, let’s look at the technical capabilities of the Gemini models. Technical Capabilities Developing the Gemini models demanded innovations in training algorithms, datasets, and infrastructure. The Pro model benefits from scalable infrastructure, completing pretraining in weeks using a fraction of Ultra's resources. The Nano series excels in distillation and training, creating top-tier small language models for diverse tasks and driving on-device experiences Let’s dive into the technical innovations: Training Infrastructure Training Gemini models involved using Tensor Processing Units (TPUs), TPUv5e, and TPUv4, with Gemini Ultra utilizing a large fleet of TPUv4 accelerators across multiple data centers. Scaling up from the prior flagship model, PaLM-2, posed infrastructure challenges, necessitating solutions for hardware failures and network communication at unprecedented scales. The “single controller” programming model of Jax and Pathways simplified the development workflow, while in-memory model state redundancy significantly improved recovery speed on unplanned hardware failures. Addressing Silent Data Corruption (SDC) challenges at this scale involved innovative techniques such as deterministic replay and proactive SDC scanners.  Training Dataset The Gemini models are trained on a diverse dataset that is both multimodal and multilingual, incorporating web documents, books, code, and media data. Utilizing the SentencePiece tokenizer, training on a large sample of the entire corpus enhances vocabulary and model performance, enabling efficient tokenization of non-Latin scripts. The dataset size for training varies based on model size, with quality and safety filters applied, including heuristic rules and model-based classifiers. Data mixtures and weights are determined through ablations on smaller models, with staged training adjusting the composition for optimal pretraining results. Gemini’s Architecture Although the researchers did not reveal complete details, they mention that the Gemini models are built on top of Transformer decoders with architecture and model optimization improvements for stable training at scale. The models are written in Jax and trained using TPUs. The architecture is similar to DeepMind's Flamingo, CoCa, and PaLI, with a separate text and vision encoder. Google Gemini: A Family of Highly Capable Multimodal Models Ethical Considerations Gemini follows a structured approach to responsible deployment to identify, measure, and manage foreseeable downstream societal impacts on the models.  Google Gemini: A Family of Highly Capable Multimodal Models Safety Testing and Quality Assurance Within the framework of responsible development, Gemini places a strong emphasis on safety testing and quality assurance. Rigorous evaluation targets, set by Google DeepMind’s Responsibility and Safety Council (RSC) across key policy domains, including but not limited to child safety, underscore Gemini's dedication to upholding ethical standards. This commitment ensures that safety considerations are integral to the development process, guaranteeing that Gemini meets the highest quality and ethical responsibility standards. Gemini Ultra is currently undergoing thorough trust and safety evaluations, including red-teaming conducted by trusted external parties. The model is further refined through fine-tuning and reinforcement learning from human feedback (RLHF) to ensure its robustness before being made widely available to users. Potential Risks and Challenges The creation of a multimodal AI model introduces specific risks. Gemini prioritizes the mitigation of these risks, covering areas such as factuality, child safety, harmful content, cybersecurity, biorisk, representation, and inclusivity. The impact assessments for Gemini encompass various aspects of the model's capabilities, systematically evaluating the potential consequences in alignment with Google’s AI Principles. Does Gemini Suffer from Hallucinations? Although Gemini's report does not explicitly reference tests involving hallucination, it details the measures taken to decrease the occurrence of such occurrences. Specifically, Gemini emphasizes instruction tuning to address this concern, concentrating on three essential behaviors aligned with real-world scenarios: attributions, closed-book response generation, and hedging. Read the technical report Gemini: A Family of Highly Capable Multimodal Models   Application & Performance Enhancements Gemini Pro x Google BARD Chatbot Google’s answer to ChatGPT, Bard is now powered by Gemini Pro. Bard is an experimental conversational AI service developed by Google, which was previously powered by LaMDA (Language Model for Dialogue Applications). It combines extensive knowledge with large language models to provide creative and informative responses, aiming to simplify complex topics and engage users in meaningful conversations. Gemini Nano x Pixel8 Pro Gemini Nano, designed for on-device applications, will be released as a feature update on the Pixel 8 Pro. This integration brings forth two enhanced features: Summarize in Recorder and Smart Reply in Gboard. Gemini Nano ensures sensitive data stays on the device, offering offline functionality. Summarize in Recorder provides condensed insights from recorded content without a network connection, while Smart Reply in Gboard, powered by Gemini Nano, suggests high-quality responses with conversational awareness. Generative Search Gemini AI will now be used for Search Generative Experience (SGE), with a 40% reduction in latency for English searches in the U.S. This enhancement accelerates the search process and elevates the quality of search results. Gemini's application in Search signifies a significant step toward a more efficient and refined generative search experience, showcasing a potential to redefine how users interact with information through Google Search.  Google Platform Integrations In the coming months, Gemini is set to extend its footprint across various Google products and services, promising enhanced functionalities and experiences. Users can anticipate Gemini's integration in key platforms such as Search, Ads, Chrome, and Duet AI What’s Next? The prospects of Gemini 1.0, as highlighted in the report, are centered around the expansive new applications and use cases enabled by its capabilities. Let’s take a closer look at what could stem from these models. Complex image understanding: Gemini's ability to parse complex images such as charts or infographics opens new possibilities in visual data interpretation and analysis. Multimodal reasoning: The model can reason over interleaved images, audio, and text sequences and generate responses that combine these modalities. This is particularly promising for applications requiring the integration of various types of information. Educational applications: Gemini's advanced reasoning and understanding skills can be applied in educational settings, potentially enhancing personalized learning and intelligent tutoring systems. Multilingual communication: Given its proficiency in handling multiple languages, Gemini could greatly improve multilingual communication and translation services. Information summarization and extraction: Gemini's ability to process and synthesize large amounts of information makes it ideal for summarization and data extraction tasks, like prior state-of-the-art models (e.g. GPT-4) Creative applications: The model's potential for creative tasks, where it can generate novel content or assist in creative processes, is also significant.

Dec 07 2023

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Florence-2: Microsoft's New Foundation Model Explained

In the world of Artificial General Intelligence (AGI) systems, a significant shift is underway toward leveraging versatile, pre trained representations that exhibit task-agnostic adaptability across diverse applications. This shift started in the field of natural language processing (NLP), and now it’s making its way into computer vision too. That’s where Florence-2 comes in: a vision foundation model designed to address the challenges of task diversity in computer vision and vision-language tasks. Background Artificial General Intelligence aims to create systems that can perform well across various tasks, much like how humans demonstrate diverse capabilities. Recent successes with versatile, pre trained models in the field of NLP have inspired a similar approach in the realm of computer vision. While existing large vision models excel in transfer learning, they often struggle when faced with various tasks and simple instructions. The challenge lies in handling spatial hierarchy and semantic granularity inherent in diverse vision-related tasks. Key challenges include the limited availability of comprehensive visual annotations and the absence of a unified pretraining framework with a singular neural network architecture seamlessly integrating spatial hierarchy and semantic granularity. Existing datasets tailored for specialized applications heavily rely on human labeling, which limits, the development of foundational models capable of capturing the intricacies of vision-related tasks. Read the blog Visual Foundation Models (VFMs) Explained to know more about large vision models.  Florence-2: An Overview To tackle these challenges head-on, the Florence-2 model emerges as a universal backbone achieved through multitask learning with extensive visual annotations. This results in a unified, prompt-based representation for diverse vision tasks, effectively addressing the challenges of limited comprehensive training data and the absence of a unified architecture. Built by Microsoft, the Florence-2 model adopts a sequence-to-sequence architecture, integrating an image encoder and a multi-modality encoder-decoder. This design accommodates a spectrum of vision tasks without the need for task-specific architectural modifications, aligning with the ethos of the NLP community for versatile model development with a consistent underlying structure. Florence-2 stands out through its unprecedented zero-shot and fine-tuning capabilities, achieving new state-of-the-art results in tasks such as captioning, object detection, visual grounding, and referring expression comprehension. Even after fine-tuning with public human-annotated data, Florence-2 competes with larger specialist models, establishing new benchmarks.  Technical Deep Dive Carefully designed to overcome the limitations of traditional single-task frameworks, Florence-2 employs a sequence-to-sequence learning paradigm, integrating various tasks under a common language modeling objective. Florence-2’s model architecture. Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks Let's dive into the key components that make up this innovative model architecture. Task Formulation  Florence-2 adopts a sequence-to-sequence framework to address a wide range of vision tasks in a unified manner. Each task is treated as a translation problem, where the model takes an input image and a task-specific prompt and generates the corresponding output response.  Tasks can involve either text or region information, and the model adapts its processing based on the nature of the task. For region-specific tasks, location tokens are introduced to the tokenizer's vocabulary list, accommodating various formats like box representation, quad box representation, and polygon representation. Vision Encoder The vision encoder plays a pivotal role in processing input images. To accomplish this, Florence-2 incorporates DaViT (Data-efficient Vision Transformer) as its vision encoder. DaViT transforms input images into flattened visual token embeddings, capturing both spatial and semantic information. The resulting visual token embeddings are concatenated with text embeddings for further processing. Multi-Modality Encoder-Decoder Transformer The heart of Florence-2 lies in its transformer-based multi-modal encoder-decoder. This architecture processes both visual and language token embeddings, enabling a seamless fusion of textual and visual information. The multi-modality encoder-decoder is instrumental in generating responses that reflect a comprehensive understanding of the input image and task prompt. Optimization Objective To train Florence-2 effectively, a standard language modeling objective is employed. Given the input (combined image and prompt) and the target output, the model utilizes cross-entropy loss for all tasks. This optimization objective ensures that the model learns to generate accurate responses across a spectrum of vision-related tasks. The Florence-2 architecture stands as a testament to the power of multi-task learning and the seamless integration of textual and visual information. Let’s discuss the multi-task learning setup briefly. Multi-Task Learning Setup Multitask learning is at the core of Florence-2's capabilities, necessitating large-scale, high-quality annotated data. The model's data engine, FLD-5B, autonomously generates a comprehensive visual dataset with 5.4 billion annotations for 126 million images. This engine employs an iterative strategy of automated image annotation and model refinement, moving away from traditional single and manual annotation approaches. The multitask learning approach incorporates three distinct learning objectives, each addressing a different level of granularity and semantic understanding:  Image-level Understanding Tasks: Florence-2 excels in comprehending the overall context of images through linguistic descriptions. Tasks include image classification, captioning, and visual question answering (VQA). Region/Pixel-level Recognition Tasks: The model facilitates detailed object and entity localization within images, capturing relationships between objects and their spatial context. This encompasses tasks like object detection, segmentation, and referring expression comprehension. Fine-Grained Visual-Semantic Alignment Tasks: Florence-2 addresses the intricate task of aligning fine-grained details between text and image. This involves locating image regions corresponding to text phrases, such as objects, attributes, or relations. By incorporating these learning objectives within a multitask framework, Florence-2 becomes adept at handling various spatial details, distinguishing levels of understanding, and achieving universal representation for vision tasks. Read the original research paper by Azure AI, Microsoft, authored by Bin Xiao, Haiping Wu, Weijian Xu, Xiyang Dai, Houdong Hu, Yumao Lu, Michael Zeng, Ce Liu, Lu Yuan available on Arxiv: Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks   Performance and Evaluation Zero-Shot and Fine-Tuning Capabilities Florence-2 impresses with its zero-shot performance, excelling in diverse tasks without task-specific fine-tuning. For instance, Florence-2-L achieves a CIDEr score of 135.6 on COCO caption, surpassing models like Flamingo with 80 billion parameters. In fine-tuning, Florence-2 demonstrates efficiency and effectiveness. Its simple design outperforms models with specialized architectures in tasks like RefCOCO and TextVQA. Florence-2-L showcases competitive state-of-the-art performance across various tasks, emphasizing its versatile capabilities. Comparison with SOTA Models Florence-2-L stands out among vision models, delivering strong performance and efficiency. Compared to models like PolyFormer and UNINEXT, Florence-2-L excels in tasks like RefCOCO REC and RES, showcasing its generalization across task levels. In image-level tasks, Florence-2 achieves a CIDEr score of 140.0 on COCO Caption karpathy test split, outperforming models like Flamingo with more parameters. Downstream tasks, including object detection and segmentation, highlight Florence-2's superior pre-training. It maintains competitive performance even with frozen model stages, emphasizing its effectiveness. Florence-2's performance in semantic segmentation tasks on the ADE20k dataset also stands out, outperforming previous state-of-the-art models like BEiT pre trained model on ViT-B. Qualitative Evaluation and Visualization Results Florence-2 is qualitatively evaluated on the following tasks: Detailed Image Caption Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks Visual Grounding Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks Open Vocabulary Detection Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks OCR Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks Region to Segmentation Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks Comparison with SOTA LMMs The Florence-2 is evaluated against other Large Multimodal Models (LMMs) like GPT 4V, LLaVA, and miniGPT-4 on detailed caption tasks. Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks Conclusion In conclusion, Florence-2 emerges as a groundbreaking vision foundation model, showcasing the immense potential of multi-task learning and the fusion of textual and visual information. It offers an efficient solution for various tasks without the need for extensive fine-tuning. The model's ability to handle tasks from image-level understanding to fine-grained visual-semantic alignment marks a significant stride towards a unified vision foundation. Florence-2's architecture, exemplifying the power of sequence-to-sequence learning, sets a new standard for comprehensive representation learning. Looking ahead, Florence-2 paves the way for the future of vision foundation models. Its success underscores the importance of considering diverse tasks and levels of granularity in training, promising more adaptable and robust machine learning models. As we navigate the evolving landscape of artificial intelligence, Florence-2's achievements open avenues for exploration, urging researchers to delve deeper into the realms of multi-task learning and cross-modal understanding. Read More Guide to Vision-Language Models (VLMs) MiniGPT-v2 Explained Top Multimodal Annotation Tools

Nov 14 2023

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An Introduction to Cross-Entropy Loss Functions

Loss functions are widely used in machine learning tasks for optimizing models. The cross-entropy loss stands out among the many loss functions available, especially in classification tasks. But why is it so significant? Cross-entropy loss is invaluable in certain scenarios, particularly when interpreting the outputs of neural networks that utilize the softmax function, a common practice in deep learning models. This loss function measures the difference between two probability distributions, reflecting how well the model predicts the actual outcomes. The term "surrogate loss" refers to an alternative loss function used instead of the actual loss function, which might be difficult to compute or optimize. In this context, cross-entropy can be considered a surrogate for other more complex loss functions, providing a practical approach for model optimization. In the broader theoretical landscape of machine learning, there's an extensive analysis of a category of loss functions, often referred to in research as "composite loss" or "sum of losses." This category includes cross-entropy (also known as logistic loss), generalized cross-entropy, mean absolute error, and others. These loss functions are integral to providing non-asymptotic guarantees and placing an upper boundary on the estimation error of the actual loss based on the error values derived from the surrogate loss. Such guarantees are crucial as they influence the selection of models or hypotheses during the learning process. Researchers have been delving into novel loss functions designed for more complex, often adversarial, machine learning environments. For instance, certain innovative loss functions have been crafted by incorporating smoothing terms into traditional forms. These "smoothed" functions enhance model robustness, especially in adversarial settings where data alterations can mislead learning processes. These advancements are paving the way for new algorithms that can withstand adversarial attacks, fortifying their predictive accuracy. Foundations of Loss Functions Loss functions are the backbone of machine learning optimization, serving as critical navigational tools that guide the improvement of models during the training process. These functions present a measure that models strive to minimize, representing the difference or 'loss' between predicted and actual known values. While the concept of maximizing a function, often referred to as a "reward function," exists, particularly in reinforcement learning scenarios, the predominant focus in most machine learning contexts is minimizing the loss function. Role in Model Optimization Central to model optimization is the gradient descent process, which adjusts model parameters iteratively to minimize the loss function. This iterative optimization is further powered by backpropagation, an algorithm that calculates the gradient of the loss function concerning the model parameters. However, the optimization landscape is fraught with challenges. One of the primary concerns is the convergence to local minima instead of the global minimum. In simple terms, while the model might think it has found the optimal solution (local minimum), there might be a better overall solution (global minimum) that remains unexplored. Explanation of minima/maxima The choice and design of loss functions are crucial for optimal training of ML tasks. For instance, cross-entropy loss, commonly used in classification tasks, has properties such as being convex and providing a clear signal for model updates, making it particularly suitable for such problems. Understanding the nuances of different loss functions, including cross-entropy loss, and their impact on model optimization is essential for developing effective machine learning models. Common Loss Functions in Machine Learning Several loss functions have been developed and refined, each tailored to specific use cases. Mean Squared Error (MSE): The mean squared error (or MSE) is a quadratic loss function that measures the average squared difference between the estimated values (predictions) and the actual value. For n samples, it is mathematically represented as  MSE Loss MSE Loss is widely used in regression problems. For instance, predicting house prices based on various features like area, number of rooms, and location. A model with a lower MSE indicates a better fit of the model to the data. Hinge Loss Hinge loss, or max-margin loss, is used for binary classification tasks. It is defined as Hinge Loss Function Here, 0 is for correct classifications, and 1 is for wrong classifications. The hinge loss is near zero if the prediction is correct and with a substantial margin from the decision boundary (high confidence). However, the loss increases as the prediction is either wrong or correct, but with a slim margin from the decision boundary. Hinge loss is commonly associated with Support Vector Machines (SVM). It's used in scenarios where a clear margin of separation between classes is desired, such as in image classification or text categorization. Log Loss (Logistic Loss) Log loss quantifies the performance of a classification model where the prediction input is a probability value between 0 and 1. It is defined as: Log Loss function The log loss penalizes both errors (false positives and false negatives), whereas the confidently wrong predictions are more severely penalized. Log loss is used in logistic regression and neural networks for binary classification problems. It's suitable for scenarios like email spam detection, where you want to assign a probability of an email being spam. Each loss function has unique characteristics and is chosen based on the problem's nature and the desired output type. How to select a loss function Regression: In regression tasks, where the goal is to predict a continuous value, the difference between the predicted and actual values is of primary concern. Common loss functions for regression include: Mean Squared Error (MSE): Suitable for problems where large errors are particularly undesirable since they are squared and thus have a disproportionately large impact. The squaring operation amplifies larger errors. Mean Absolute Error (MAE): Useful when all errors, regardless of magnitude, are treated uniformly. Classification: In classification tasks, where the goal is to categorize inputs into classes, the focus is on the discrepancy between the predicted class probabilities and the actual class labels. Common loss functions for classification include: Log Loss (Logistic Loss): Used when the model outputs a probability for each class, especially in binary classification. Hinge Loss: Used for binary classification tasks, especially with Support Vector Machines, focusing on maximizing the margin. Cross-Entropy Loss: An extension of log loss to multi-class classification problems. The selection of a loss function is not one-size-fits-all. It requires a deep understanding of the problem, the nature of the data, the distribution of the target variable, and the specific goals of the analysis. Entropy in Information Theory Entropy in information theory measures the amount of uncertainty or disorder in a set of probabilities. It quantifies the expected value of the information contained in a message and is foundational for data compression and encryption. Shannon's Entropy Shannon's entropy, attributed to Claude Shannon, quantifies the uncertainty in predicting a random variable's value. It is defined as: Shannon Entropy Shannon's entropy is closely related to data compression. It represents the minimum number of bits needed to encode the information contained in a message, which is crucial for lossless data compression algorithms. When the entropy is low (i.e., less uncertainty), fewer bits are required to encode the information, leading to more efficient compression. Shannon's entropy is foundational for designing efficient telecommunications coding schemes and developing compression algorithms like Huffman coding. Kullback-Leibler Divergence Kullback-Leibler (KL) Divergence measures how one probability distribution diverges from a second, expected probability distribution. It is defined as KL Divergence Equation Here are the parameters and their meanings: P: The true probability distribution, which serves as the reference. Q: The approximate probability distribution is being compared to P. x: The event or outcome for which the probabilities are defined. P(x): The probability of event x according to the true distribution P. Q(x): The probability of event x according to the distribution Q. DKL ( p || q ): The KL Divergence quantifies the difference between the two distributions. KL Divergence is used in model evaluation to measure the difference between predicted probability and true distributions. It is especially useful in scenarios like neural network training, where the goal is to minimize the divergence between the predicted and true distributions. KL Divergence is often used for model comparison, anomaly detection, and variational inference methods to approximate complex probability distributions. Cross-Entropy: From Theory to Application Mathematical Derivation Cross-entropy is a fundamental concept in information theory that quantifies the difference between two probability distributions. It builds upon the foundational idea of entropy, which measures the uncertainty or randomness of a distribution. The cross-entropy between two distributions, P and Q, is defined as: Cross Entropy between P & Q P(x) is the probability of event x in distribution P, and Q(x) is the probability of event x in distribution Q. 1. Log-likelihood function and maximization: The log-likelihood measures how well a statistical model predicts a sample. In machine learning, maximizing the log-likelihood is equivalent to minimizing the cross-entropy between the true data distribution and the model's predictions. 2. Relationship with Kullback-Leibler divergence: The Kullback-Leibler (KL) divergence is another measure of how one probability distribution differs from a second reference distribution. Cross-entropy can be expressed in terms of KL divergence and the entropy of the true distribution: Where H(p) is the entropy of distribution p, and DKL(p || q) is the KL divergence between distributions p and q. Binary vs. Multi-Class Cross-Entropy Cross-entropy is a pivotal loss function in classification tasks, measuring the difference between two probability distributions. Cross-entropy formulation varies depending on the nature of the classification task:  binary or multi-class. Binary Cross-Entropy: This is tailored for binary classification tasks with only two possible outcomes. Given \( y \) as the actual label (either 0 or 1) and \( \hat{y} \) as the predicted probability of the label being 1, the binary cross-entropy loss is articulated as: This formulation captures the divergence of the predicted probability from the actual label. Categorical Cross-Entropy: Suited for multi-class classification tasks, this formulation is slightly more intricate. If \( P \) represents the true distribution over classes and \( Q \) is the predicted distribution, the categorical cross-entropy is given by: Categorical Cross-Entropy Loss Here, the loss is computed over all classes, emphasizing the divergence of the predicted class probabilities from the true class distribution. Challenges in Multi-Class Scenarios:  The complexity of multi-class cross-entropy escalates with an increase in the number of classes. A fundamental challenge is ensuring that the predicted probabilities across all classes aggregate to one. This normalization is typically achieved using the softmax function, which exponentiates each class score and then normalizes these values to yield a valid probability distribution. While binary and multi-class cross-entropy aim to measure the divergence between true and predicted distributions, their mathematical underpinnings and associated challenges differ based on the nature of the classification task. Practical Implications of Cross-Entropy Loss Cross-entropy loss is pivotal in optimizing models, especially in classification tasks. The implications of cross-entropy loss are vast and varied, impacting the speed of model convergence and regularization (to mitigate overfitting). Impact on Model Convergence Speed of Convergence: Cross-entropy loss is preferred in many deep learning tasks because it often leads to faster convergence than other loss functions. It amplifies the gradient when the predicted probability diverges significantly from the actual label, providing a stronger signal for the model to update its weights and thus encouraging faster learning. Avoiding Local Minima: The nature of the cross-entropy loss function helps models avoid getting stuck in local minima.. Cross-entropy loss penalizes incorrect predictions more heavily than other loss functions, which encourages the model to continue adjusting its parameters significantly until it finds a solution that generalizes well rather than settling for a suboptimal fit. Local Minima Regularization and Overfitting L1 and L2 Regularization: You can combine regularization techniques like L1 (Lasso) and L2 (Ridge) with cross-entropy loss to prevent overfitting.  L1 regularization tends to drive some feature weights to zero, promoting sparsity, while L2 shrinks weights, preventing any single feature from overshadowing others. These techniques add penalty terms to the loss function, discouraging the model from assigning too much importance to any feature. Dropout and its effect on cross-entropy: Dropout is a regularization technique where random subsets of neurons are turned off during training. This prevents the model from becoming overly reliant on any single neuron. When combined with cross-entropy loss, dropout can help the model generalize better to unseen data. Implementing Cross-Entropy in Modern Frameworks PyTorch In PyTorch, the `nn.CrossEntropyLoss()` function is used to compute the cross-entropy loss. It's important to note that the input to this loss function should be raw scores (logits) and not the output of a softmax function because it combines the softmax activation function and the negative log-likelihood loss in one class.  import tensorflow as tf loss_fn = tf.keras.losses.CategoricalCrossentropy() For binary classification tasks, `tf.keras.losses.BinaryCrossentropy()` is more appropriate: loss_fn_binary = tf.keras.losses.BinaryCrossentropy() Custom Loss Functions: TensorFlow and Keras provide flexibility in defining custom loss functions. This can be useful when the standard cross-entropy loss needs to be modified or combined with another loss function for specific applications. Advanced Topics in Cross-Entropy Label Smoothing Label smoothing is a regularization technique that prevents the model from becoming too confident about its predictions. Instead of using hard labels (e.g., [0, 1]), it uses soft labels (e.g., [0.1, 0.9]) to encourage the model to be less certain, distributing certainty between classes. Improving model generalization: Label smoothing can improve the generalization capability of models by preventing overfitting. Overfitting occurs when a model becomes too confident about its predictions based on the training data, leading to poor performance on unseen data. By using soft labels, label smoothing encourages the model to be less certain, which can lead to better generalization. Implementation and results: Most deep learning frameworks have label smoothing built-in implementations. For instance, in TensorFlow, it can be achieved by adding a small constant to the true labels and subtracting the same constant from the false labels. The results of using label smoothing can vary depending on the dataset and model architecture. Still, it can generally lead to improved performance, especially in cases where the training data is noisy or imbalanced. Cross Entropy Loss fn with Label Smoothing Focal Loss and Class Imbalance Focal loss is a modification of the standard cross-entropy loss designed to address the class imbalance problem. In datasets with imbalanced classes, the majority class can dominate the loss, leading to poor performance for the minority class. Focal Loss and Cross-Entropy Equation Origins and Context: The paper "Focal Loss for Dense Object Detection" delves into the challenges faced by one-stage object detectors, which have historically lagged behind the accuracy of two-stage detectors despite their potential for speed and simplicity. The authors identify the extreme foreground-background class imbalance during the training of dense detectors as the primary culprit. The core idea behind Focal Loss is to reshape the standard cross-entropy loss in a way that down-weights the loss assigned to well-classified examples. This ensures that the training focuses more on a sparse set of hard-to-classify examples, preventing the overwhelming influence of easy negatives. Addressing the class imbalance problem: Focal loss adds a modulating factor to the cross-entropy loss, which down-weights the loss contribution from easy examples (i.e., examples from the majority class) and up-weights the loss contribution from hard examples (i.e., examples from the minority class). This helps the model focus more on the minority class, leading to better performance on imbalanced datasets. Performance Implications: By focusing more on the minority class, focal loss can lead to improved performance on minority classes without sacrificing performance on the majority class. This makes it a valuable tool for tasks where the minority class is particularly important, such as medical diagnosis or fraud detection. Focal Loss Formula The parameters are: p_t is the model's estimated probability for the class with the true label t. alpha: A balancing factor, typically between 0 and 1, which can be set differently for each class. gamma: A focusing parameter, typically greater than 0, reduces the relative loss for well-classified examples, focusing more on hard, misclassified examples. Cross Entropy: Key Takeaways Cross-Entropy Loss as a Performance Measure: Cross-entropy loss is crucial in classification tasks because it quantifies the difference between the predicted probability distribution of the model and the actual distribution of the labels. It is particularly effective when combined with the softmax function in neural networks, providing a clear gradient signal that aids in faster and more efficient model training. Role of Loss Functions in Optimization: Loss functions like cross-entropy guide the training of machine learning models by providing a metric to minimize. The design of these functions, such as the convexity of cross-entropy, is essential to avoid local minima and ensure that the model finds the best possible parameters for accurate predictions. Handling Class Imbalance with Focal Loss: Focal loss is an adaptation of cross-entropy that addresses class imbalance by focusing training on hard-to-classify examples. It modifies the standard cross-entropy loss by adding a factor that reduces the contribution of easy-to-classify examples, thus preventing the majority class from overwhelming the learning process. Regularization Techniques to Prevent Overfitting: Combining cross-entropy loss with regularization techniques like L1 and L2 regularization, or dropout, can prevent overfitting. These methods add penalty terms to the loss function or randomly deactivate neurons during training, encouraging the model to generalize to new, unseen data. Label Smoothing for Improved Generalization: Label smoothing is a technique that uses soft labels instead of hard labels during training, which prevents the model from becoming overly confident about its predictions. This can lead to better generalization to unseen data by encouraging the model to distribute its certainty among the possible classes rather than focusing too narrowly on the classes observed in the training set.

Nov 07 2023

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Training vs. Fine-tuning: What is the Difference?

Training and fine-tuning are crucial stages in the machine learning model development lifecycle, serving distinct purposes. This article explains the intricacies of both methodologies, highlighting their differences and importance in ensuring optimal model performance. Training in the context of deep learning and neural networks refers to the phase where a new model learns from a dataset. During this phase, the model adjusts its model weights based on the input data and the corresponding output, often using embeddings and activation functions. While embeddings and activation functions play significant roles in certain model architectures and tasks, they are not universally employed during the training phase of all deep learning models. It's crucial to understand the specific context and model architecture to determine their relevance.  The objective is to diminish the discrepancy between the anticipated and factual output, frequently termed error or loss. This is predominantly achieved using algorithms like backpropagation and optimization techniques like gradient descent. Fine-tuning, conversely, follows the initial training, where a pre-trained model (previously trained on a vast dataset like ImageNet) is trained on a smaller, task-specific dataset. The rationale is to leverage the knowledge the model has acquired from the initial training process and tailor it to a more specific task. This becomes invaluable, especially when the new dataset for the new task is limited, as training from scratch might lead to overfitting. As training stars, the neural network's weights are randomly initialized or set using methods like He or Xavier initialization. These weights are fundamental in determining the model's predictions. As the training progresses, these weights adjust to minimize the error, guided by a specific learning rate.  Conversely, during fine-tuning, the model starts with pre-trained weights from the initial training, which are then fine-tuned to suit the new task better, often involving techniques like unfreezing certain layers or adjusting the batch size. The training aims to discern patterns and features from the data, creating a base model that excels on unseen data and is often validated using validation sets. Fine-tuning, however, zeroes in on adapting a generalized model for a specific task, often leveraging transfer learning to achieve this. While training focuses on generalizing models, fine-tuning refines this knowledge to cater to specific tasks, making it a crucial topic in NLP with models like BERT, computer vision tasks like image classification, and, more recently, the proliferation of foundation models. Learn more: Visual Foundation Models (VFMs) by Lead ML Engineer at Encord, Frederik Hvilshøj. The Training Process Initialization of Weights Random Initialization In deep learning, initializing the weights of neural networks is crucial for the training process. Random initialization is a common method where weights are assigned random values. This method ensures a break in symmetry among neurons, preventing them from updating similarly during backpropagation. However, random initialization can sometimes lead to slow convergence or the vanishing gradient problem. He or Xavier Initialization Specific strategies, like He or Xavier initialization, have been proposed to address the challenges of random initialization. He initialization, designed for ReLU activation functions, initializes weights based on the size of the previous layer, ensuring that the variance remains consistent across layers. On the other hand, Xavier initialization, suitable for tanh activation functions, considers the sizes of the current and previous layers. These methods help with faster and more stable convergence. Backpropagation and Weight Updates Gradient Descent Variants Backpropagation computes the gradient of the loss function concerning each weight by applying the chain rule. Various gradient descent algorithms update the weights and minimize the loss. The most basic form is the Batch Gradient Descent. However, other variants like Stochastic Gradient Descent (SGD) and Mini-Batch Gradient Descent have been introduced to improve efficiency and convergence. Role of Learning Rate The learning rate is a hyperparameter that dictates the step size during weight updates. A high learning rate might overshoot the optimal point, while a low learning rate might result in slow convergence. Adaptive learning rate methods like Adam, RMSprop, and Adagrad adjust the learning rate during training, facilitating faster convergence without manual tuning. Regularization Techniques Dropout Overfitting is a common pitfall in deep learning, where the model performs exceptionally well on the training data but needs to improve on unseen data. Dropout is a regularization technique that mitigates overfitting. During training, random neurons are "dropped out" or deactivated at each iteration, ensuring the model does not rely heavily on any specific neuron. Dropout Neural Networks L1 and L2 Regularization L1 and L2 are other regularization techniques that add a penalty to the loss function. L1 regularization adds a penalty equivalent to the absolute value of the weights' magnitude, which aids feature selection. L2 regularization adds a penalty based on the squared magnitude of weights, preventing weights from reaching extremely high values. Both methods help in preventing overfitting, penalizing complex models, and producing a more generalized model. L1 and L2 Regualization The Fine-tuning Process Transfer Learning: The Backbone of Fine-tuning Transfer learning is a technique where a model developed for a task is adapted for a second related task. It is a popular approach in deep learning where pre-trained models are used as the starting point for computer vision and natural language processing tasks due to the extensive computational resources and time required to train models from scratch. Pre-trained models save the time and resources needed to train a model from scratch. They have already learned features from large datasets, which can be leveraged for a new task with a smaller dataset. This is especially useful when acquiring labeled data is challenging or costly. When fine-tuning, it's common to adjust the deeper layers of the model while keeping the initial layers fixed. The rationale is that the initial layers capture generic features (like edges or textures), while the deeper layers capture more task-specific patterns. However, the extent to which layers are fine-tuned can vary based on the similarity between the new task and the original task. Strategies for Fine-tuning One of the key strategies in fine-tuning is adjusting the learning rates. A lower learning rate is often preferred because it makes the fine-tuning process more stable. This ensures the model retains the previously learned features without drastic alterations.  Another common strategy is freezing the initial layers of the model during the fine-tuning process. This means that these layers won't be updated during training. As mentioned, the initial layers capture more generic features, so fixing them is often beneficial. Applications and Use Cases Domain Adaptation Domain adaptation refers to the scenario where the source and target tasks are the same, but the data distributions differ. Fine-tuning can be used to adapt a model trained on source data to perform well on target data. Domain Adaptation Data Augmentation Data augmentation involves creating new training samples by applying transformations (like rotations, scaling, and cropping) to the existing data. Combined with fine-tuning, it can improve the model's performance, especially when the available labeled data is limited. Data Augmentation Comparative Analysis Benefits of Training from Scratch Customization: Training a model from scratch allows complete control over its architecture, making it tailored specifically for the task. No Prior Biases: Starting from scratch ensures the model doesn't inherit any biases or unwanted features from pre-existing datasets. Deep Understanding: Training a model from the ground up can provide deeper insights into the data's features and patterns, leading to a more robust model for specific datasets. Optimal for Unique Datasets: For datasets significantly different from existing ones, training from scratch might yield better results as the model learns features unique to that dataset. Limitations of Training from Scratch  This approach requires more time as the model learns features from the ground up and requires a large, diverse dataset for optimal performance. With the right data and regularization, models can easily fit. Extended Training Time: Starting from the basics means the model has to learn every feature, leading to prolonged training durations. Data Dependency: Achieving optimal performance mandates access to a vast and varied dataset, which might only sometimes be feasible. Risk of Overfitting: Without adequate data and proper regularization techniques, models can overfit, limiting their generalization capabilities on unseen data. Advantages of Fine-Tuning Efficiency in Training: Utilizing pre-trained models can expedite the training process, as they have already grasped foundational features from extensive datasets. Data Economy: Since the model has undergone training on vast datasets, fine-tuning typically demands a smaller amount of data, making it ideal for tasks with limited datasets. Limitations of Fine-Tuning Compatibility Issues: Ensuring that the input and output formats, as well as the architectures and frameworks of the pre-trained model, align with the new task can be challenging. Overfitting: Fine-tuning on a small dataset can lead to overfitting, which reduces the model's ability to generalize to new, unseen data. Knowledge Degradation: There's a risk that the model might forget some of the features and knowledge acquired during its initial training, a phenomenon often referred to as "catastrophic forgetting." Bias Propagation: Pre-trained models might carry inherent biases. When fine-tuned, these biases can be exacerbated, especially in applications that require high sensitivity, such as facial recognition. Optimizing your hyperparameters is a key process for getting your pre-trained models to learn the dataset during fine-tuning. Interested in learning more about hyperparameter optimization while fine-tuning models? Check out our article.   Research Breakthroughs Achieved Through Fine-tuning Fine-tuning in NLP BERT (Bidirectional Encoder Representations from Transformers) has been a cornerstone in the NLP community. Its architecture allows for capturing context from both directions (left-to-right and right-to-left) in a text, making it highly effective for various NLP tasks.  In 2023, we have seen advancements in BERT and its variants. One such development is "Ferret: Refer and Ground Anything Anywhere at Any Granularity." This Multimodal Large Language Model (MLLM) can understand the spatial reference of any shape or granularity within an image and accurately ground open-vocabulary descriptions. Such advancements highlight the potential of fine-tuning pre-trained models like BERT to achieve specific tasks with high precision. Fine-tuning in Computer Vision Models like ResNet and VGG have been foundational in computer vision. These architectures, with their deep layers, have been pivotal in achieving state-of-the-art results on various image classification tasks. In 2023, a significant breakthrough, "Improved Baselines with Visual Instruction Tuning," was introduced. This research emphasized the progress of large multimodal models (LMM) with visual instruction tuning. Such advancements underscore the importance of fine-tuning in adapting pre-trained models to specific tasks or datasets, enhancing their performance and utility. Training vs Fine-tuning: Key Takeaways Training and fine-tuning are pivotal processes in deep learning and machine learning. While training involves initializing model weights and building a new model from scratch using a dataset, fine-tuning leverages pre-trained models and tailors them to a specific task.  Opting for training from scratch is ideal when you have a large dataset vastly different from available pre-trained models like those on Imagenet. It's also the preferred strategy when there's an absence of pre-existing models on platforms like TensorFlow Hub, PyTorch Zoo, or Keras that align with the task. On the flip side, fine-tuning is advantageous when the dataset at hand is smaller or when the new task mirrors the objectives of the pre-trained model. This approach, backed by optimization techniques like adjusting the learning rate, allows for swifter convergence and frequently culminates in superior performance, especially in scenarios with limited training data. Future Trends and Predictions: The deep learning community, including platforms like OpenAI, is progressively gravitating towards fine-tuning, especially with the advent of large language models and transformers. This inclination is anticipated to persist, especially with the ascent of transfer learning and the triumph of models like BERT in NLP and ResNet in computer vision. As neural networks evolve and datasets expand, hybrid methodologies that amalgamate the strengths of both training and fine-tuning paradigms may emerge, potentially blurring the demarcation between the two.

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Mean Average Precision in Object Detection

Object detection is a fascinating field in computer vision. It is tasked with locating and classifying objects within an image or video frame. The challenge lies in the model's ability to identify objects of varying shapes, sizes, and appearances, especially when they are partially occluded or set against cluttered backgrounds. Deep learning has proven highly effective in object detection. Through training, deep learning models extract features like shape, size, and texture from images to facilitate object detection. They can also learn to classify objects based on the extracted features. One widely used deep learning model for object detection is YOLO (You Only Look Once). YOLO is a single-shot object detection algorithm, meaning it detects objects in a single pass through the image. This makes YOLO very fast, but it can be less accurate than two-stage object detection algorithms. Another renowned deep learning model for object detection is SSD (Single Shot MultiBox Detector). SSD is similar to YOLO but uses a distinct approach to detecting objects. SSD partitions the image into a grid of cells, and each cell predicts potential bounding boxes for objects that may be present in the cell. This makes SSD more accurate than YOLO, but it is also slower. Object detection typically involves two primary components: Object Classification: Assigning labels or categories, such as "car", "person", or "cat", to detected objects. Object Localization: Identifying the object's position within the image, typically represented by a bounding box. These bounding boxes are described using coordinates (x, y) for the top-left corner, along with their dimensions (width, height). Evaluation Metrics for Object Detection Assessing the performance, effectiveness, and limitations of object detection models is pivotal. You can employ several evaluation metrics to assess the accuracy and robustness of these models: Mean Average Precision (mAP) averages the precision and recall scores for each object class to determine the overall accuracy of the object detector. Intersection over Union (IoU) measures the overlap between the predicted bounding box and the ground-truth bounding box. A score of 1.0 signifies a perfect overlap, whereas a score of 0.0 denotes no overlap between the predicted and the ground truth bounding boxes. False Positive Rate (FPR) measures the ratio of incorrect positive predictions to the total number of actual negatives. In simpler terms, it quantifies how often the model mistakenly predicts the presence of an object within a bounding box when there isn't one. False Negative Rate (FNR) measures the ratio of missed detections to the total number of actual objects. Essentially, it evaluates how often the model fails to detect an object when it is indeed present in the image. The choice of evaluation metric must align with the goals and nuances of the specific application. For instance, in traffic monitoring applications, mAP and IoU might be prioritized. Conversely, in medical imaging, where false alarms and missed detections can have serious implications, metrics such as FPR and FNR become highly significant. Importance of Evaluating Object Detection Models The evaluation of object detection models is critically important for a myriad of reasons: Performance Assessment: Given that object detection models operate in complex real-world scenarios—with factors like diverse lighting conditions, occlusions, and varying object sizes—it's essential to determine how well they cope with such challenges. Model Selection and Tuning: Not all object detection models perform well. Evaluating different models helps in selecting the most suitable one for a specific application. By comparing their performance metrics, you can make informed decisions about which model to use and whether any fine-tuning is necessary. Benchmarking: Object detection is a rapidly evolving field with new algorithms and architectures being developed regularly.  Understanding Limitations: Object detection models might perform well on some object classes but struggle with others. Evaluation helps identify which classes are challenging for the model and whether its performance is consistent across different object categories. Safety and Reliability: In critical applications such as autonomous driving, surveillance, and medical imaging, the accuracy of object detection directly impacts safety outcomes. Quality Control: Faulty object detection in industrial settings can precipitate production mishaps or equipment malfunctions. Periodic evaluation ensures models remain reliable. User Confidence: For users and stakeholders to trust object detection systems, you need to consistently validate capabilities. Iterative Improvement: Evaluation feedback is crucial for iterative model improvement. Understanding where a model fails or performs poorly provides insights into areas that need further research, feature engineering, or data augmentation. Legal and Ethical Considerations: Biased or flawed object detection can sometimes lead to legal and ethical ramifications, underscoring the importance of thorough evaluation. Resource Allocation: In resource-limited settings, evaluations guide the efficient distribution of computational resources, ensuring the best model performance. New to object detection? Check out this short article on object detection, the models, use cases, and real-world applications.   Overview of mAP Mean average precision (mAP) is a metric used to evaluate the performance of object detection models. It is calculated by averaging the precision-recall curves for each object class. Precision quantifies the fraction of true positives out of all detected objects, while recall measures the fraction of true positives out of all actual objects in the image. The AUC is a measure of the model's overall performance for that class, and it considers both precision and recall. By averaging these areas across all classes, we obtain mAP. The AUC score can be used to calculate the area under the precision-recall curve to get one number that describes model performance.  mAP is a popular metric for evaluating object detection models because it is easy to understand and interpret. It is also relatively insensitive to the number of objects in the image. A high mAP score indicates that the model can detect objects with both high precision and recall, which is critical in applications like autonomous driving where reliable object detection is pivotal to avoiding collisions. A perfect mAP score of 1.0 suggests that the model has achieved flawless detection across all classes and recall thresholds. Conversely, a lower mAP score signifies potential areas of improvement in the model's precision and/or recall. How to Calculate Mean Average Precision (mAP) 1. Generate the prediction scores using the model. 2. Convert the prediction scores to class labels. 3. Calculate the confusion matrix. 4. Calculate the precision and recall metrics. 5. Calculate the area under the precision-recall curve (AUC) for each class. 6. Average the AUCs to get the mAP score. Practical Applications mAP is a widely used metric for evaluating object detection models in a variety of applications, such as: Self-driving Cars Self-driving cars are one of the most promising applications of object detection technology. To safely navigate the road, self-driving cars need to be able to detect and track various objects, including pedestrians, cyclists, other vehicles, and traffic signs. mAP is a valuable metric for evaluating the performance of object detection models for self-driving cars because it takes into account both precision and recall. Source Precision is the fraction of detected objects that are actually present in the image or video, i.e., correct detections. Recall, on the other hand, measures how many of the actual objects in the image were successfully detected by the model..  High precision indicates fewer false positives, ensuring that the model isn't mistakenly identifying objects that aren't there. Conversely, high recall ensures the model detects most of the real objects in the scene.For self-driving cars, a high mAP is essential for ensuring safety. If the model is not able to detect objects accurately, it could lead to accidents.  Visual Search Visual search is a type of information retrieval that allows users to find images or videos that contain specific objects or scenes. It is a practical application of mean average precision (mAP) because mAP can be used to evaluate the performance and reliability of visual search algorithms. In visual search, the primary objective is to retrieve images or videos that are relevant to the user's query. This can be a challenging task, as there may be millions or even billions of images or sequences of videos available. To address this challenge, visual search algorithms use object detection models to identify the objects in the query image or video.  Object detection models play a pivotal role by identifying potential matches, and generating a list of candidate images or videos that seem to contain the queried objects. The mAP metric can be used to evaluate the performance of the object detection models by measuring the accuracy and completeness of the candidate lists. Interested in building visual search applications? Learn how to build semantic visual search with ChatGPT and CLIP in this webinar.   Medical Image Analysis Source mAP is used to evaluate the performance of object detection models in medical image analysis. It is calculated by taking the average of the precision-recall curves for all classes. The higher the mAP, the better the performance of the model. How to Calculate mAP The following code shows how to calculate mAP in Python: import numpy as np import pandas as pd import matplotlib.pyplot as plt import sklearn.metrics This code above imports essential libraries for our machine learning tasks and data visualization. The imported libraries are used for numerical operations (numpy), data manipulation (pandas), model evaluation (`precision_score` and `recall_score` from `sklearn.metrics`), and creating plots (`matplotlib.pyplot`).   Create two different datasets containing binary data. The code below defines two sets of data for binary classification model evaluations. Each set consists of ground truth labels (`y_true_01`) and predicted scores (`pred_scores_01`). y_true_01 = ["positive", "negative", "positive", "negative", "positive", "positive", "positive", "negative", "positive", "negative"] pred_scores_01 = [0.7, 0.3, 0.5, 0.6, 0.55, 0.9, 0.75, 0.2, 0.8, 0.3] `y_true_02` is a list of ground truth labels for a set of instances. In this case, the labels are either "positive" or "negative," representing the two classes in a binary classification problem. `pred_scores_02` is a list of predicted scores or probabilities assigned by a classification model to the instances in `y_true_01`. These scores represent the model's confidence in its predictions. y_true_02 = ["negative", "positive", "positive", "negative", "negative", "positive", "positive", "positive", "negative", "positive"] pred_scores_02 = [0.32, 0.9, 0.5, 0.1, 0.25, 0.9, 0.55, 0.3, 0.35, 0.85] `y_true_02` is another list of ground truth labels for a different set of instances. `pred_scores_02` is a list of predicted scores or probabilities assigned by a classification model to the instances in `y_true_02`. Set a threshold value with a range of 0.2 to 0.9 and a 0.05 step. Setting a threshold value with a range of 0.2 to 0.9 and a 0.05 step is a good practice for calculating mean average precision (mAP) because it allows you to see how the model performs at different levels of confidence.  thresholds = np.arange(start=0.2, stop=0.9, step=0.05) `precision_recall_curve()` function computes precision and recall ratings for various binary classification thresholds. The function accepts as inputs threshold values, projected scores, and ground truth labels (`y_true`, `pred_scores`, and `thresholds`). The thresholds are iterated through, predicted labels are generated, precision and recall scores are calculated, and the  results are then reported. Finally, lists of recall and precision values are returned. def precision_recall_curve(y_true, pred_scores, thresholds): precisions = [] recalls = [] for threshold in thresholds: y_pred = ["positive" if score >= threshold else "negative" for score in pred_scores]  precision = sklearn.metrics.precision_score(y_true=y_true,  y_pred=y_pred, pos_label="positive")  recall = sklearn.metrics.recall_score(y_true=y_true, y_pred=y_pred,  pos_label="positive") precisions.append(precision)  recalls.append(recall) return precisions, recalls Calculate the average precision scores for the first dataset (`y_true_01`) and plot out the result. precisions, recalls = precision_recall_curve(y_true=y_true_01, pred_scores=pred_scores_01, thresholds=thresholds) plt.plot(recalls, precisions, linewidth=4, color="red", zorder=0) #Set the label and the title for the precision-recall curve plot plt.xlabel("Recall", fontsize=12, fontweight='bold') plt.ylabel("Precision", fontsize=12, fontweight='bold') plt.title("Precision-Recall Curve", fontsize=15, fontweight="bold") plt.show() # Append values to calculate area under the curve (AUC) precisions.append(1)recalls.append(0) precisions = np.array(precisions) recalls = np.array(recalls) precisions = np.array(precisions) recalls = np.array(recalls) # Calculate the AP avg_precision_class01= np.sum((recalls[:-1] - recalls[1:]) * precisions[:-1]) print('============================================') print('Average precision score:',np.round(avg_precision_class01,2)) Output: The AP score of 0.95 is a good score; it indicates that the model performs relatively well in terms of precision when varying the classification threshold and measuring the trade-off between precision and recall. Now, let’s calculate the average precision scores for the second dataset (`y_true_02`) and plot out the result. # Calculate precision and recall values for different threshold precisions, recalls = precision_recall_curve(y_true=y_true_02, pred_scores=pred_scores_02, thresholds=thresholds) # Plot the precision-recall curve plt.plot(recalls, precisions, linewidth=4, color="blue", zorder=0) #Set the label and the title for the precision-recall curve plot plt.xlabel("Recall", fontsize=12, fontweight='bold') plt.ylabel("Precision", fontsize=12, fontweight='bold') plt.title("Precision-Recall Curve", fontsize=15, fontweight="bold") plt.show() # Append values to calculate area under the curve (AUC) precisions.append(1) recalls.append(0) #Convert precision and recall lists to Numpy arrays for computation precisions = np.array(precisions) recalls = np.array(recalls) # Calculate the AP avg_precision_class02 = np.sum((recalls[:-1] - recalls[1:]) * precisions[:-1]) print('============================================') print('Average precision score:',np.round(avg_precision_class02,2)) Output: For the second dataset, the AP score was 0.96 which is also a good score. It indicates that the model is able to identify positive samples with high precision and high recall. Calculating the Mean Average Precision (mAP) The mean Average Precision or mAP score is calculated by taking the mean AP over all classes and/or overall IoU thresholds, depending on the different detection challenges that exist. The formula for MAP: # Number of classes or labels (in this case, 2 classes) num_labels = 2 # Calculate the Mean Average Precision (mAP) by averaging the AP scores for both classes mAP = (avg_precision_class2 + avg_precision_class1) / num_labels # Print the Mean Average Precision score print('Mean average Precision score:', np.round(mAP, 3)) Output:  For class 1, you calculated an Average Precision (AP) score of 0.89, which indicates how well your model performs in terms of precision and recall for class 1. For class 2, you calculated an Average Precision (AP) score of 0.81, which indicates the performance of your model for class 2. You calculate the mAP score by averaging these AP scores for all classes. In this specific scenario, you averaged the AP scores for classes 1 and 2. Challenges and Limitations of mAP mAP is a widely used metric for evaluating the performance of object detection and instance segmentation algorithms. However, it has its own set of challenges and limitations that should be considered when interpreting its results: Sensitivity to IoU Threshold: The mAP calculation is sensitive to the chosen IoU threshold for matching ground truth and predicted boxes. Different applications might require different IoU thresholds, and using a single threshold might not be appropriate for all scenarios. Uneven Distribution of Object Sizes: mAP treats all object instances equally, regardless of their sizes. Algorithms might perform well on larger objects but struggle with smaller ones, leading to an imbalance in the evaluation. You can check out this helpful resource.  Ignoring Object Categories: mAP treats all object categories with the same importance. In real-world applications, some categories might be more critical than others, and this factor isn't reflected in mAP. Handling Multiple Object Instances: mAP focuses on evaluating the detection of individual instances of objects. It might not accurately reflect an algorithm's performance when multiple instances of the same object are closely packed together. Difficulty in Handling Overlapping Objects: When objects overlap significantly, it can be challenging to determine whether the predicted bounding boxes match the ground truth. This situation can lead to inaccuracies in mAP calculations. Doesn't Account for Execution Speed: mAP doesn't consider the computational efficiency or execution speed of an algorithm. In real-time applications, the speed of detection might be as crucial as its accuracy. Complexity of Calculations: The mAP calculation involves multiple steps, including sorting, precision-recall calculations, and interpolation. These steps can be complex and time-consuming to implement correctly. Mean Average Precision (mAP): Key Takeaways  Mean Average Precision (mAP) is an essential metric for evaluating object detection models' performance. Calculated through precision and recall values, mAP provides a comprehensive assessment of detection accuracy, aiding model selection, improvement, and benchmarking.  mAP is a good metric to use for applications where it is important to both detect objects and avoid false positives. A high mAP score is important for ensuring that the model can reliably detect objects. It has applications in self-driving cars, visual search, medical image analysis, and lots more. Deep learning techniques, exemplified by architectures like YOLO (You Only Look Once), aim to improve object detection performance, potentially leading to higher mAP scores in evaluations and contributing to advancements in various domains. Throughout this article, we've explored the inner workings of mAP, uncovering its mathematical underpinnings and its significance in assessing object detection performance.  Armed with this knowledge, you are better equipped to navigate the complex landscape of object detection, armed with the ability to make informed decisions when designing, training, and selecting models for specific applications.

Nov 05 2023

sampleImage_vision-language-models-guide

Guide to Vision-Language Models (VLMs)

For quite some time, the idea that artificial intelligence (AI) could understand visual and textual cues as effectively as humans seemed far-fetched and unimaginable.  However, with the emergence of multimodal AI, we are seeing a revolution where AI can simultaneously comprehend various modalities, such as text, image, speech, facial expressions, physiological gestures, etc., to make sense of the world around us. The ability to process multiple modalities has opened up various avenues for AI applications. One exciting application of multimodal AI is Vision-Language Models (VLMs). These models can process and understand the modalities of language (text) and vision (image) simultaneously to perform advanced vision-language tasks, such as Visual Question Answering (VQA), image captioning, and Text-to-Image search. In this article, you will learn about:  VLM architectures. VLM evaluation strategies. Mainstream datasets used for developing vision-language models. Key challenges, primary applications, and future trends of VLMs. Let’s start by understanding what vision-language models are. 🔥 NEW RELEASE: We released TTI-Eval (text-to-image evaluation), an open-source library for evaluating zero-shot classification models like CLIP and domain-specific ones like BioCLIP against your (or HF) datasets to estimate how well the model will perform. Get started with it on GitHub, and do ⭐️ the repo if it's awesome. 🔥 What Are Vision Language Models? A vision-language model is a fusion of vision and natural language models. It ingests images and their respective textual descriptions as inputs and learns to associate the knowledge from the two modalities. The vision part of the model captures spatial features from the images, while the language model encodes information from the text. The data from both modalities, including detected objects, the spatial layout of the image, and text embeddings, are mapped to each other. For example, if the image contains a bird, the model will learn to associate it with a similar keyword in the text descriptions. This way, the model learns to understand images and transforms the knowledge into natural language (text) and vice versa. Training VLMs Building VLMs involves pre-training foundation models and zero-shot learning. Transfer learning techniques, such as knowledge distillation, can be used to fine-tune the models for more specific downstream tasks. These are simpler techniques that require smaller datasets and less training time while maintaining decent results. Modern frameworks, on the other hand, use various techniques to get better results, such as Contrastive learning. Masked language-image modeling. Encoder-decoder modules with transformers and more. These architectures can learn complex relations between the various modalities and provide state-of-the-art results. Let’s discuss these in detail. Vision Language Models: Architectures and Popular Models Let’s look at some VLM architectures and learning techniques that mainstream models such as CLIP, Flamingo, and VisualBert, among others, use. Contrastive Learning Contrastive learning is a technique that learns data points by understanding their differences. The method computes a similarity score between data instances and aims to minimize contrastive loss. It’s most useful in semi-supervised learning, where only a few labeled samples guide the optimization process to label unseen data points. Contrastive Learning For example, one way to understand what a cat looks like is to compare it to a similar cat image and a dog image. Contrastive learning models learn to distinguish between a cat and a dog by identifying features such as facial structure, body size, and fur. The models can determine which image is closer to the original, called the “anchor,” and predict its class. CLIP is an example of a model that uses contrastive learning by computing the similarity between text and image embeddings using textual and visual encoders. It follows a three-step process to enable zero-shot predictions. Trains a text and image encoder during pretraining to learn the image-text pairs. Converts training dataset classes into captions. Estimates the best caption for the given input image for zero-shot prediction. CLIP Architecture VLMs like CLIP power the semantic search feature within Encord Active. When you log into Encord → Active → Choose a Project → Use the Natural Language search to find items in your dataset with a text description. Here is a way to search with natural language using “White sneakers” as the query term: Read the full guide, ‘How to Use Semantic Search to Curate Images of Products with Encord Active,' in this blog post. ALIGN is another example that uses image and textual encoders to minimize the distance between similar embeddings using a contrastive loss function. PrefixLM PrefixLM is an NLP learning technique mostly used for model pre-training. It inputs a part of the text (a prefix) and learns to predict the next word in the sequence. In Visual Language Models, PrefixLM enables the model to predict the next sequence of words based on an image and its respective prefix text. It leverages a Vision Transformer (ViT) that divides an image into a one-dimensional patch sequence, each representing a local image region. Then, the model applies convolution or linear projection over the processed patches to generate contextualized visual embeddings. For text modality, the model converts the text prefix relative to the patch into a token embedding. The transformer's encoder-decoder blocks receive both visual and token embeddings. It is there that the model learns the relationships between the embeddings. SimVLM is a popular architecture utilizing the PrefixLM learning methodology. It has a simpler Transformer architecture than its predecessors, surpassing their results in various benchmarks. It uses a transformer encoder to learn image-prefix pairs and a transformer decoder to generate an output sequence. The model also demonstrates good generalization and zero-shot learning capabilities. SimVLM Architecture Similarly, VirTex uses a convolutional neural network to extract image features and a textual head with transformers to manage text prefixes. You can train the model end-to-end to predict the correct image captions by feeding image-text pairs to the textual head. VirTex Architecture Frozen PrefixLM While PrefixLM techniques require training visual and textual encoders from scratch, Frozen PrefixLM allows you to use pre-trained networks and only update the parameters of the image encoders. For instance, the architecture below shows how Frozen works using a pre-trained language model and visual encoder. The text encoder can belong to any large language model (LLM), and the visual encoder can also be a pre-trained visual foundation model. You can fine-tune the image encoder so its image representations align with textual embeddings, allowing the model to make better predictions. Frozen Architecture Flamingo's architecture uses a more state-of-the-art (SOTA) approach. It uses a CLIP-like vision encoder and an LLM called Chinchilla. Keeping the LLM fixed lets you train the visual encoder on images interleaved between texts. The visual encoders process the image through a Perceiver Sampler. The technique results in faster inference and makes Flamingo ideal for few-shot learning. Flamingo Architecture Multimodal Fusing with Cross-Attention This method utilizes the encoders of a pre-trained LLM for visual representation learning by adding cross-attention layers. VisualGPT is a primary example that allows quick adaptation of an LLM’s pre-trained encoder weights for visual tasks. VisualGPT Architecture Practitioners extract relevant objects from an image input and feed them to a visual encoder. The resulting visual representations are then fed to a decoder and initialized with weights according to pre-trained LLM. The decoder module balances the visual and textual information through a self-resurrecting activation unit (SRAU). The SRAU method avoids the issue of vanishing gradients, a common problem in deep learning where model weights fail to update due to small gradients. As such, VisualGPT outperforms several baseline models, such as the plain transformer, the Attention-on-Attention (AoA) transformer, and the X-transformer. Masked-language Modeling (MLM) & Image-Text Matching (ITM) MLM works in language models like BERT by masking or hiding a portion of a textual sequence and training the model to predict the missing text. ITM involves predicting whether sentence Y follows sentence X. You can adapt the MLM and ITM techniques for visual tasks. The diagram below illustrates VisualBERT's architecture, trained on the COCO dataset. VisualBERT Architecture It augments the MLM procedure by introducing image sequences and a masked textual description. Based on visual embeddings, the objective is to predict the missing text. Similarly, ITM predicts whether or not a caption matches the image. No Training You can directly use large-scale, pre-trained vision-language models without any fine-tuning. For example, MAGIC and ASIF are training-free frameworks that aim to predict text descriptions that align closely with the input image.  MAGIC uses a specialized score based on CLIP-generated image embeddings to guide language models' output. Using this score, an LLM generates textual embeddings that align closely with the image semantics, enabling the model to perform multimodal tasks in a zero-shot manner. ASIF uses the idea that similar images have similar captions. The model computes the similarities between the training dataset's query and candidate images. Next, it compares the query image embeddings with the text embeddings of the corresponding candidate images. Then, it predicts a description whose embeddings are the most similar to those of the query image, resulting in comparable zero-shot performance to models like CLIP and LiT. ASIF Prediction Strategy Knowledge Distillation This technique involves transferring knowledge from a large, well-trained teacher model to a lighter student model with few parameters. This methodology allows researchers to train VLMs from larger, pre-trained models. For instance, ViLD is a popular VLM developed using the knowledge distillation methodology. The model uses a pre-trained open-vocabulary image classification model as the teacher to train a two-stage detector (student). The model matches textual embeddings from a textual encoder with image embeddings. ViLD Architecture Knowledge distillation transfers knowledge from the image encoder to the backbone model to generate regional embeddings automatically. Only the backbone model generates regional embeddings during inference, and it matches them with unseen textual embeddings. The objective is to draw correct bounding boxes around objects in an image based on textual descriptions. Evaluating Vision Language Models VLM validation involves assessing the quality of the relationships between the image and text data. For an image captioning model, this would mean comparing the generated captions to the ground-truth description. You can use various automated n-gram-based evaluation strategies to compare the predicted labels in terms of accuracy, semantics, and information precision. Below are a few key VLM evaluation metrics. BLEU: The Bilingual Evaluation Understudy (BLEU) metric was originally proposed to evaluate machine translation tasks. It computes the precision of the target text compared to a reference (ground truth) by considering how many words in the candidate sentence appear in the reference.  ROUGE: Recall-Oriented Understudy for Gisting Evaluation (ROUGE) computes recall by considering how many words in the reference sentence appear in the candidate. METEOR: Metric for Evaluation of Translation with Explicit Ordering (METEOR) computes the harmonic mean of precision and recall, giving more weight to recall and multiplying it with a penalty term. The metric is an improvement over others that work with either Precision or Recall, as it combines information from both to give a better evaluation. CIDEr: Consensus-based Image Description Evaluation (CIDEr) compares a target sentence to a set of human sentences by computing the average similarity between reference and target sentences using TF-IDF scores. 🔥 NEW RELEASE: We released TTI-Eval (text-to-image evaluation), an open-source library for evaluating zero-shot classification models like CLIP and domain-specific ones like BioCLIP against your (or HF) datasets to estimate how well the model will perform. Get started with it on GitHub, and do ⭐️ the repo if it's awesome. 🔥 Now that you have learned evaluation metrics pertinent to Vision-Language Models (VLMs), knowing how to curate datasets for these models is essential. A suitable dataset provides fertile ground for training and validating VLMs and is pivotal in determining the models' performance across diverse tasks. Datasets for Vision Language Models Collecting training data for VLMs is more challenging than collecting data for traditional AI models since it involves collecting and quality-assuring multiple data modalities. Below is a list of several datasets combining images and text for multimodal training. LAION-5B: Practitioners use the LAION-5B dataset to build large, pre-trained VLMs. The dataset contains over five billion image-text pairs generated from CLIP, with descriptions in English and foreign languages, catering to a multilingual domain. PMD: The Public Model Dataset (PMD) originally appeared in the FLAVA paper and contains 70 billion image-text pairs. It is a collection of data from other large-scale datasets, such as COCO, Conceptual Captions (CC), RedCaps, etc. This dataset is a reservoir of multimodal data that fosters robust model training. VQA: Experts use the VQA dataset to fine-tune pre-trained VLMs for downstream VQA and visual reasoning tasks. The dataset contains over 200,000 images, with five questions per image, ten ground-truth answers, and three incorrect answers per question. ImageNet: ImageNet contains over 14 million images with annotations categorized according to the WordNet hierarchy. It’s helpful in building models for simple downstream tasks, such as image classification and object recognition. Despite the availability of high-quality multimodal datasets, VLMs can face significant challenges during the model development process. Let’s discuss them below. Limitations of Vision Language Models Although VLMs are powerful in understanding visual and textual modalities to process information, they face three primary challenges: Model complexity. Dataset bias. Evaluation difficulties. Model Complexity Language and vision models are quite complex on their own, and combining the two only worsens the problem. Their complexity raises additional challenges in acquiring powerful computing resources for training, collecting large datasets, and deploying on weak hardware such as IoT devices. Dataset Bias Dataset biases occur when VLMs memorize deep patterns within training and test sets without solving anything. For instance, training a VLM on images curated from the internet can cause the model to memorize specific patterns and not learn the conceptual differences between various images. Evaluation Strategies The evaluation strategies discussed above only compare a candidate sentence with reference sentences. The approach assumes that the reference sentences are the only ground truths. However, a particular image can have several ground-truth descriptions. Although consensus-based metrics like CIDEr account for the issue, using them becomes challenging when consensus is low for particular images. Another challenge is when a generic description applies to several images. Spurious Correlation As the illustration shows, a VLM can annotate or retrieve several relevant images that match the generic caption. However, in reality, the model is nothing more than a bag-of-words. All it’s doing is considering words, such as ‘city,’ ‘bus,’ ‘lights,’ etc., to describe the image instead of actually understanding the caption's sequential order and true contextual meaning. Furthermore, VLMs used for VQA can generate highly confident answers to nonsensical questions. For instance, asking a VLM, “What color is the car?” for an image that contains a white horse will generate the answer as “white” instead of pointing out that there isn’t a car in the picture. Lastly, VLMs lack compositional generalization. This means that their performance decreases when they process novel concepts. For example, a VLM can fail to recognize a yellow horse as a category since it’s rare to associate the color yellow with horses. Despite many development and deployment challenges, researchers and practitioners have made significant progress in adopting VLMs to solve real problems. Let’s discuss them briefly below. Applications of Vision Language Models While most VLMs discussed earlier are helpful in captioning images, their utility extends to various domains that leverage the capability to bridge visual and linguistic modalities. Here are some additional applications: Image Retrieval: Models such as FLAVA help users navigate through image repositories by helping them find relevant photos based on linguistic queries. An e-commerce site is a relevant example. Visitors can describe what they’re looking for in a search bar, and a VLM will show the suitable options on the screen. This application is also popular on smartphones, where users can type in keywords (landscapes, buildings, etc.) to retrieve associated images from the gallery. Generative AI: Image generation through textual prompts is a growing domain where models like DALL-E allow users to create art or photos based on their descriptions. The application is practical in businesses where designers and inventors want to visualize different product ideas. It also helps create content for websites and blogs and aids in storytelling. Segmentation: VLMs like SegGPT help with segmentation tasks such as instance, panoptic, semantic, and others. SegGPT segments an image by understanding user prompts and exploiting a distinct coloring scheme to segment objects in context. For instance, users can ask SegGPT to segment a rainbow from several images, and SegGPT will efficiently annotate all rainbows. [Video] Frederik and Justin discussed how Visual-Language Models (VLMs) power AI in different industries, including their efficiency over Large Language Models (LLMs). Future Research The following are a few crucial future research directions in the VLM domain: Better Datasets The research community is working on building better training and test datasets to help VLMs with compositional understanding. CLEVR is one example of this effort. CLEVR Dataset As the illustration shows, it contains images of novel shapes, colors, and corresponding questions that allow experts to test a VLM’s visual reasoning capacity. Better Evaluation Methods Evaluation challenges warrant in-depth research into better evaluation methods for building more robust VLMs. One alternative is to test VLMs for individual skills through the ARO benchmark. Attribute identification, relational reasoning, and word-order sensitivity (ARO) are three skills that VLMs must master. ARO Dataset The illustration above explains what ARO entails in different contexts. Using such a dataset, experts can analyze what VLMs learn and how to improve the outcomes. 🔥 NEW RELEASE: We released TTI-Eval (text-to-image evaluation), an open-source library for evaluating zero-shot classification models like CLIP and domain-specific ones like BioCLIP against your (or HF) datasets to estimate how well the model will perform. Get started with it on GitHub, and do ⭐️ the repo if it's awesome. 🔥 Robotics Researchers are also using VLMs to build purpose-specific robots. Such robots can help navigate environments, improve warehouse operations in manufacturing by monitoring items, and enhance human-machine interaction by allowing robots to understand human gestures, such as facial expressions, body language, voice tones, etc. Medical VQA VLMs’ ability to annotate images and recognize complex objects can help healthcare professionals with medical diagnoses. For example, they can ask VLMs critical questions about X-rays or MRI scans to determine potential problems early. Vision-Language Models: Key Takeaways Visual language modeling is an evolving field with great promise for the AI industry. Below are a few critical points regarding VLMs: Vision-language models are a multimodal architecture that simultaneously comprehends image and text data modalities. They use CV and NLP models to correlate information (embeddings) from the two modalities. Several VLM architectures exist that aim to relate visual semantics to textual representations. Although users can evaluate VLMs using automated scores, better evaluation strategies are crucial to building more reliable models. VLMs have many industrial use cases, such as robotics, medical diagnoses, chatbots, etc.

Nov 03 2023

sampleImage_llava-large-language-vision-assistant

LLaVA, LLaVA-1.5, and LLaVA-NeXT(1.6) Explained

Microsoft has recently entered the realm of multimodal models with the introduction of LLaVA, a groundbreaking solution that combines a vision encoder and Vicuna to enable visual and language comprehension. LLaVA showcases impressive chat capabilities, rivaling Open AI’s multimodal GPT-4, and sets a new benchmark for state-of-the-art accuracy in Science QA. The convergence of natural language and computer vision has led to significant advancements in artificial intelligence. While fine-tuning techniques have greatly improved the performance of large language models (LLMs) in handling new tasks, applying these methods to multimodal models remains relatively unexplored. The research paper "Visual Instruction Tuning" introduces an innovative approach called LLAVA (Large Language and Vision Assistant). It leverages the power of GPT-4, initially designed for text-based tasks, to create a new paradigm of multimodal instruction-following data that seamlessly integrates textual and visual components. In this blog, we will delve into the evolution of visual instruction tuning and explore the specifics of LLaVA, along with its recent iterations, LLaVA-1.5 and LLaVA-1.6 (or LLaVA-NeXT). By examining these advancements, we can gain valuable insights into the continuous progress of LLMs in AI. 🔥 NEW RELEASE: We released TTI-Eval (text-to-image evaluation), an open-source library for evaluating zero-shot classification models like CLIP and domain-specific ones like BioCLIP against your (or HF) datasets to estimate how well the model will perform. Get started with it on GitHub, and do ⭐️ the repo if it's awesome. 🔥 What is Visual Instruction Tuning? Visual instruction tuning is a technique that involves fine-tuning a large language model (LLM) to understand and execute instructions based on visual cues. This approach aims to connect language and vision, enabling AI systems to comprehend and act upon human instructions involving both modalities. For instance, imagine asking a machine learning model to describe an image, perform an action in a virtual environment, or answer questions about a scene in a photograph. Visual instruction tuning equips the model to perform these tasks effectively. LLaVA vs. LLaVA-1.5 LLaVA LLaVA, short for Large Language and Vision Assistant, is one of the pioneering multimodal models. Despite being trained on a relatively small dataset, LLaVA showcases exceptional abilities in understanding images and responding to questions about them. Its performance on tasks that demand deep visual comprehension and instruction-following is particularly impressive. Notably, LLaVA demonstrates behaviors akin to multimodal models like GPT-4, even when presented with unseen images and instructions. LLaVA Architecture LLaVA Architecture LLaVA utilizes the LLaMA model, which is renowned for its efficacy in open-source language-only instruction-tuning projects. LLaVA relies on the pre-trained CLIP visual encoder ViT-L/14 for visual content processing, which excels in visual comprehension. The encoder extracts visual features from input images and connects them to language embeddings through a trainable projection matrix. This projection effectively translates visual features into language embedding tokens, thereby bridging the gap between text and images. Read the original paper by Microsoft, authored by Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee, available on Arxiv: Visual Instruction Tuning.   LLaVA Training LLaVA's training encompasses two essential stages that enhance its capacity to comprehend user instructions, understand both language and visual content, and generate accurate responses:  Pre-training for Feature Alignment: LLaVA aligns visual and language features to ensure compatibility in this initial stage.  Fine-tuning End-to-End: The second training stage focuses on fine-tuning the entire model. While the visual encoder's weights remain unchanged, both the projection layer's pre-trained weights and the LLM's parameters become subject to adaptation. This fine-tuning can be tailored to different application scenarios, yielding versatile capabilities. LLaVA-1.5 In LLaVA-1.5, there are two significant improvements. Firstly, adding an MLP vision-language connector enhances the system's capabilities. Secondly, integrating academic task-oriented data further enhances its performance and effectiveness. MLP Vision-Language Connector LLaVA-1.5 builds upon the success of MLPs in self-supervised learning and incorporates a design change to enhance its representation power. The transition from a linear projection to a two-layer MLP significantly enhances LLaVA-1.5's multimodal capabilities. This modification has profound implications, enabling the model to effectively understand and interact with both language and visual elements. Academic Task-Oriented Data LLaVA-1.5 goes beyond its predecessor by integrating VQA datasets designed for academic tasks. These datasets focus on specific tasks related to VQA, Optical Character Recognition (OCR), and region-level perception. This enhancement equips LLaVA-1.5 to excel in various applications, including text recognition and precise localization of fine-grained visual details. Improved Baselines with Visual Instruction Tuning  The development from LLaVA to LLaVA-1.5 signifies Microsoft’s continuous pursuit to refine and expand the capabilities of large multimodal models. LLaVA-1.5 signifies a significant progression towards developing more sophisticated and adaptable AI assistants, aligning with their commitment to advancing the field of artificial intelligence. The codebase on LLaVA’s Github contains the model and the dataset (available on HuggingFace) used for training.   LLaVA 1.6 (LLaVA-NeXT) In addition to LLaVA 1.5, which uses the Vicuna-1.5 (7B and 13B) LLM backbone, LLaVA 1.6 considers more LLMs, including Mistral-7B and Nous-Hermes-2-Yi-34B. These LLMs possess nice properties, flexible commercial use terms, strong bilingual support, and a larger language model capacity. It allows LLaVA to support a broader spectrum of users and more scenarios in the community. The LLaVA recipe works well with various LLMs and scales up smoothly with the LLM up to 34B. Here are the performance improvements LLaVA-NeXT has over LLaVA-1.5: Increasing the input image resolution to 4x more pixels. This allows it to grasp more visual details. It supports three aspect ratios, up to 672x672, 336x1344, and 1344x336 resolution. Better visual reasoning and zero-shot OCR capability with multimodal document and chart data. Improved visual instruction tuning data mixture with a higher diversity of task instructions and optimizing for responses that solicit favorable user feedback. Better visual conversation for more scenarios covering different applications. Better world knowledge and logical reasoning. Efficient deployment and inference with SGLang. Along with performance improvements, LLaVA-NeXT maintains the minimalist design and data efficiency of LLaVA-1.5. It re-uses the pre-trained connector of LLaVA-1.5 and still uses less than 1 million visual instruction tuning samples. See the updated LLaVA-1.5 technical report for more details. Comparison with SOTA Multimodal AI has witnessed significant advancements, and the competition among different models is fierce. Evaluating the performance of LLaVA and LLaVA-1.5 compared to state-of-the-art (SOTA) models offers valuable insights into their capabilities. LLaVA's ability to fine-tune LLaMA using machine-generated instruction-following data has shown promising results on various benchmarks. In tasks such as ScienceQA, LLaVA achieved an accuracy that closely aligns with the SOTA model's performance. ability to handle out-of-domain questions highlights its proficiency in comprehending visual content and effectively answering questions. However, LLaVA demonstrates exceptional proficiency in comprehending and adhering to instructions within a conversational context. It's capable of reasoning and responding to queries that align with human intent, outperforming other models like BLIP-2 and OpenFlamingo. Visual Instruction Tuning The introduction of LLaVA-1.5 and its potential improvements indicate promising advancements in the field.  The collaboration between LLaVA and GPT-4 through model ensembling holds the potential for enhanced accuracy and underscores the collaborative nature of AI model development. LLaVA-Next (LLaVA 1.6) compares with SoTA methods (GPT-4V, Gemini, and LLaVA 1.5) on benchmarks for instruction-following LMMs. LLaVA-1.6 achieves improved reasoning, OCR, and world knowledge and exceeds Gemini Pro on several benchmarks. See the full result on this page. LLaVA-NeXT: Improved reasoning, OCR, and world knowledge Recent Developments LLaVA-Med LLaVA-Med, the Large Language and Vision Assistant for BioMedicine, is a groundbreaking multimodal assistant designed specifically for healthcare. This innovative model aims to support biomedical practitioners in pursuing knowledge and insights by effectively addressing open-ended research inquiries related to biomedical images. What sets LLaVA-Med apart is its cost-effective approach, leveraging a comprehensive dataset of biomedical figure-caption pairs sourced from PubMed Central.  Self-guided learning facilitated by GPT-4 excels in capturing the nuances of open-ended conversational semantics and aligning them with the specialized vocabulary of the biomedical domain. Remarkably, LLaVA-Med can be trained in less than 15 hours and exhibits exceptional capabilities in multimodal conversation. This represents a significant advancement in enhancing the comprehension and communication of biomedical images. LLaVA-Interactive LLaVA-Interactive is an all-in-one demo that showcases multimodal models' visual interaction and generation capabilities beyond language interaction. This interactive experience, which uses LLaVA, SEEM, and GLIGEN, eloquently illustrates the limitless versatility innate in multimodal models. Multimodal Foundation Models Multimodal Foundation Models: From Specialists to General-Purpose Assistants is a comprehensive 118-page survey that explores the evolution and trends in multimodal foundation models. This survey provides insights into the current state of multimodal AI and its potential applications. It is based on the tutorial in CVPR 2023 by Microsoft and the members of the LLaVA project.  Instruction Tuning with GPT-4 Vision The paper Instruction Tuning with GPT-4 discusses an attempt to use GPT-4 data for LLM self-instruct tuning. This project explores GPT-4's capabilities and potential for enhancing large language models. While LLaVA represents a significant step forward in the world of large multimodal models, the journey is far from over, and there are promising directions to explore for its future development: Data Scale: LLaVA's pre-training data is based on a subset of CC3M, and its fine-tuning data draws from a subset of COCO. One way to enhance its concept coverage, especially with regard to entities and OCR, is to consider pre-training on even larger image-text datasets.  Integrating with more computer vision models: LLaVA has shown promising results, even approaching the capabilities of the new ChatGPT in some scenarios. To advance further, one interesting avenue is the integration of powerful vision models, such as SAM.  🔥 NEW RELEASE: We released TTI-Eval (text-to-image evaluation), an open-source library for evaluating zero-shot classification models like CLIP and domain-specific ones like BioCLIP against your (or HF) datasets to estimate how well the model will perform. Get started with it on GitHub, and do ⭐️ the repo if it's awesome. 🔥 LLaVA: Key Takeaways LLaVA Challenges GPT-4: Microsoft's LLaVA is a powerful multimodal model rivaling GPT-4, excelling in chat capabilities and setting new standards for Science QA. Visual Instruction Tuning Advances AI: LLaVA's visual instruction tuning enables AI to understand and execute complex instructions involving both text and images. LLaVA-1.5 Enhancements: LLaVA-1.5 introduces an MLP vision-language connector and academic task-oriented data, boosting its ability to interact with language and visual content. Bridging Language and Vision: LLaVA's architecture combines LLaMA for language tasks and CLIP visual encoder ViT-L/14 for visual understanding, enhancing multimodal interactions.

Oct 17 2023

sampleImage_gpt4-vision

Exploring GPT-4 Vision: First Impressions

OpenAI continues to demonstrate its commitment to innovation with the introduction of GPT Vision.  This exciting development expands the horizons of artificial intelligence, seamlessly integrating visual capabilities into the already impressive ChatGPT. These strides reflect OpenAI’s substantial investments in machine learning research and development, underpinned by extensive training data.  In this blog, we'll break down the GPT-4Vision system card, exploring these groundbreaking capabilities and their significance for users. GPT-4 Vision Capabilities: Visual Inputs After the exciting introduction of GPT-4 in March, there was growing anticipation for an iteration of ChatGPT that would incorporate image integration capabilities. GPT-4 has recently become accessible to the public through a subscription-based API, albeit with limited usage initially.  Recently OpenAI released GPT-4V(ision) and has equipped ChatGPT with image understanding. ChatGPT's image understanding is powered by a combination of multimodal GPT-3.5 and GPT-4 models. Leveraging their adept language reasoning skills, these models proficiently analyze a diverse range of visuals, spanning photographs, screenshots, and documents containing both text and images. Just days prior, OpenAI's Sam Altman unveiled DALL-E 3, an AI tool that facilitates the generation of images from text inputs, harnessing the power of ChatGPT. Read OpenAI’s DALL-E 3 Explained: Generate Images with ChatGPT for more information. In a recent demonstration video featuring OpenAI's co-founder Greg Brockman, the capabilities of GPT-4's vision-related functions took center stage. Over the course of this year, GPT-4V has undergone rigorous testing across a multitude of applications, consistently delivering remarkable results, yielding remarkable results.  In the following section, we share key findings from our team's comprehensive evaluations of GPT-4V in diverse computer vision tasks: Object Detection GPT4-Vision  is able to provide accurate information about objects and perform tasks like object counting, showcasing its proficiency in comprehensive image analysis and understanding. For example, in the image below, identifying humans in the image prompt is not easy. But it performs well and also identifies the problem in the detection as well. Image from Unsplash as prompt in GPT4-Vision Visual Question Answering GPT4-Vision performs well in handling follow-up questions on the image prompt. For example, when presented with a meal photograph, it adeptly identifies all the ingredients and can provide insightful suggestions or information. This underscores its capacity to elevate user experiences and deliver valuable insights. Image from Unsplash as prompt in GPT4-Vision GPT4-Vision Multiple Condition Processing It also possesses the capability to read and interpret multiple instructions simultaneously. For instance, when presented with an image containing several instructions, it can provide a coherent and informative response, showcasing its versatility in handling complex queries. Figuring out multiple parking sign rules using GPT4-Vision Data Analysis GPT-4 excels in data analysis. When confronted with a graph and tasked with providing an explanation, it goes beyond mere interpretation by offering insightful observations that significantly enhance data comprehension and analysis. Graph from GPT-4 Technical Report GPT4-Vision Deciphering Text GPT-4 is adept at deciphering handwritten notes, even when they pose a challenge for humans to read. In challenging scenarios, it maintains a high level of accuracy, with just two minor errors. Using GPT4-Vision to decipher JRR Tolkien’s letter GPT-4 Vision Capabilities: Outperforms SOTA LLMs In casual conversations, differentiating between GPT-3.5 and GPT-4 may appear subtle, but the significant contrast becomes evident when handling more intricate instructions. GPT-4 distinguishes itself as a superior choice, delivering heightened reliability and creativity, particularly when confronted with instructions of greater complexity.  To understand this difference, extensive benchmark testing was conducted, including simulations of exams originally intended for human test-takers. These benchmarks included tests like the Olympiads and AP exams, using publicly available 2022–2023 editions and without specific training for the exams. GPT-4 Technical Report The results further reveal that GPT-4 outperforms GPT-3.5, showcasing notable excellence across a spectrum of languages, including low-resource ones such as Latvian, Welsh, and Swahili. GPT-4 Technical Report OpenAI has leveraged GPT-4 to make a significant impact across multiple functions, from support and sales to content moderation and programming. Additionally, it plays a crucial role in aiding human evaluators in assessing AI outputs, marking the initiation of the second phase in OpenAI's alignment strategy GPT-4 Vision Capabilities: Enhanced Steerability OpenAI has been dedicated to enhancing different facets of their AI, with a particular focus on steerability.  In contrast to the fixed personality traits, verbosity, and style traditionally linked to ChatGPT, developers and soon-to-be ChatGPT users now have the ability to customize the AI's style and tasks to their preferences. This customization is achieved through the utilization of 'system' messages, which enable API users to personalize their AI's responses within predefined limits. This feature empowers API users to significantly personalize their AI's responses within predefined bounds. OpenAI acknowledges the continuous need for improvement, particularly in addressing the occasional challenges posed by system messages. They actively encourage users to explore and provide valuable feedback on this innovative functionality.  GPT-4 Vision: Limitation While GPT-4 demonstrates significant advancements in various aspects, it's important to recognize the limitations of its vision capabilities.  In the field of computer vision, GPT-4, much like its predecessors, encounters several challenges: Reliability Issues GPT-4 is not immune to errors when interpreting visual content. It can occasionally "hallucinate" or produce inaccurate information based on the images it analyzes. This limitation highlights the importance of exercising caution, especially in contexts where precision and accuracy are of utmost importance. Overreliance On occasion, GPT-4 may generate inaccurate information, adhere to erroneous facts, or experience lapses in task performance.  What is particularly concerning is its capacity to do so convincingly, which could potentially lead to overreliance, with users placing undue trust in its responses and risking undetected errors.  To mitigate this, OpenAI recommends a multifaceted approach, including comprehensive documentation, responsible developer communication, and promoting user scrutiny.  While GPT-4 has made strides in steerability and refined refusal behavior, it may at times provide hedged responses, inadvertently fostering a sense of overreliance. Complex Reasoning Complex reasoning involving visual elements can still be challenging for GPT-4.  It may face difficulties with nuanced, multifaceted visual tasks that demand a profound level of understanding.  For example, when tasked with solving an easy-level New York Times Sudoku puzzle, it misinterprets the puzzle question and consequently provides incorrect results. Solving NY Times puzzle-easy on GPT4-Vision Notice Row5Column3 and Row6Column3 where it should be 4 and 5 it reads it as 5 and 1. Can you find more mistakes? Read A Guide to Building a Sudoku Solver CV Project if you don’t want to solve the sudoku on your own!   GPT-4 Vision: Risk and Mitigation GPT-4, similar to its predecessors, carries inherent risks within its vision capabilities, including the potential for generating inaccurate or misleading visual information. These risks are amplified by the model's expanded capabilities.  In an effort to assess and address these potential concerns, OpenAI collaborated with over 50 experts from diverse fields to conduct rigorous testing, putting the model through its paces in high-risk areas that demand specialized knowledge. To mitigate these risks, GPT-4 employs an additional safety reward signal during Reinforcement Learning from Human Feedback (RLHF) training. This signal serves to reduce harmful outputs by teaching the model to refuse requests for unsafe or inappropriate content. The reward signal is provided by a classifier designed to judge safety boundaries and completion style based on safety-related prompts.  While these measures have substantially enhanced GPT-4's safety features compared to its predecessor, challenges persist, including the possibility of "jailbreaks" that could potentially breach usage guidelines. Read Guide to Reinforcement Learning from Human Feedback (RLHF) for Computer Vision for information on RLHF.   GPT-4 Vision: Access OpenAI Evals In its initial GPT-4 release, OpenAI emphasized its commitment to involving developers in the development process. To further this engagement, OpenAI has now open-sourced OpenAI Evals, a powerful software framework tailored for the creation and execution of benchmarks to assess models like GPT-4 at a granular level. Evals serves as a valuable tool for model development, allowing the identification of weaknesses and the prevention of performance regressions. Furthermore, it empowers users to closely monitor the evolution of various model iterations and facilitates the integration of AI capabilities into a wide array of applications. A standout feature of Evals is its adaptability, as it supports the implementation of custom evaluation logic. OpenAI has also provided predefined templates for common benchmark types, streamlining the process of creating new evaluations.  The ultimate goal is to encourage the sharing and collective development of a wide range of benchmarks, covering diverse challenges and performance aspects. ChatGPT Plus ChatGPT Plus subscribers now have access to GPT-4 on chat.openai.com, albeit with a usage cap.  OpenAI plans to adjust this cap based on demand and system performance. As traffic patterns evolve, there's the possibility of introducing a higher-volume subscription tier for GPT-4. OpenAI may also provide some level of free GPT-4 queries, enabling non-subscribers to explore and engage with this advanced AI model. API To gain API access, you are required to join the waitlist. However, for researchers focused on studying the societal impact of AI, there is an opportunity to apply for subsidized access through OpenAI's Researcher Access Program. GPT-4 Vision: Key Takeaways ChatGPT is now powered by visual capabilities making it more versatile. GPT-4 Vision can be used for various computer vision tasks like deciphering written texts, OCR, data analysis, object detection, etc. Still has limitations like hallucination similar to GPT-3.5. However, the overreliance is reduced compared to GPT-3.5 because of enhanced steerability. It’s available now to ChatGPT Plus users!

Oct 16 2023

sampleImage_scale-alternatives

5 Alternatives to Scale AI

The AI landscape has been revolutionized with the advent of tools and platforms that offer enhanced functionality and real-time capabilities. Founded by Alexandr Wang, Scale AI has emerged as a key player, offering a template for high-quality data infrastructure.  As with any industry leader, however, new entrants offer a fresh perspective and more cost-effective solutions.  As we delve into the best alternatives to Scale AI, we'll explore platforms that offer an enhanced user experience, specializing in data labeling, cater to large-scale operations, and can handle many users. From platforms that leverage neural networks to those that focus on transcription, the future of AI is diverse and promising. Encord  Encord offers a suite of tools designed to accelerate the creation of training data. Encord's annotation platform is powered by AI-assisted labeling, enabling users to develop high-quality training data and deploy models up to 10 times faster. Encord’s active learning toolkit allows you to evaluate your models, and curate the most valuable data for labeling. ML Pipeline & Features State-of-the-art AI-assisted labeling and workflow tooling platform powered by micro-models Perfect for image, video, DICOM, and SAR annotation, labeling, QA workflows, and training computer vision models Native support for a wide range of annotation types, including bounding box, polygon, polyline, instance segmentation, keypoints, classification, and more Easy collaboration, annotator management, and QA workflows to track annotator performance and ensure high-quality labels Utilizes quality metrics to evaluate and improve ML pipeline performance across data collection, labeling, and model training stages Effortlessly search and curate data using natural language search across images, videos, DICOM files, labels, and metadata Auto-detect and resolve dataset biases, errors, and anomalies like outliers, duplication, and labeling mistakes Export, re-label, augment, review, or delete outliers from your dataset Robust security functionality with label audit trails, encryption, and compliance with FDA, CE, and HIPAA regulations Expert data labeling services on-demand for all industries Advanced Python SDK and API access for seamless integration and easy export into JSON and COCO formats Integration and Compatibility Encord offers robust integration capabilities, allowing users to import data from their preferred storage buckets and build pipelines for annotation, validation, model training, and auditing. The platform also supports programmatic automation, ensuring seamless workflows and efficient data operations. Benefits and Customer Feedback Its users have received Encord positively, with many highlighting the platform's efficiency in reducing false acceptance rates and its ability to train models on high-qualitydatasets. The platform's emphasis on AI-assisted labeling and active learning has been particularly appreciated, ensuring accurate and rapid training data creation. Learn more about how computer vision teams use Encord Vida reduce their model false positive from 6% to 1% Floy reduce CT & MRI annotation times by ~50% Stanford Medicine reduce experiment time by 80% King's College London increase labeling efficiency by 6.4x Tractable go through hyper-growth supported by faster annotation operations  iMerit iMerit specializes in providing data annotation solutions, including those for LiDAR, which is crucial for applications like autonomous vehicles and robotics. With a focus on complex data types, iMerit ensures high precision and quality in its annotations, making it a preferred choice for industries that require intricate data labeling. ML Pipeline and Features Expertise in LiDAR data annotation, ensuring accurate and high-quality annotations While Scale AI is known for its broad range of data labeling services, iMerit's strength lies in its specialization in complex data types, most notably LiDAR Robust integration options, allowing seamless connection with various platforms and tools Various tools and platforms for efficient data annotation and management Emphasis on compliance and data protection, ensuring that businesses can trust them with their sensitive data Benefits and Customer Feedback iMerit has garnered positive feedback from its clientele, particularly for its expertise in LiDAR data annotation. Many users have highlighted the platform's precision, efficiency, and quality of annotations. The platform's ability to handle complex data types and provide tailored solutions has been particularly appreciated, making it a go-to solution for industries like autonomous driving and robotics. Refer to the G2 Link for customer feedback on the iMerit platform. Dataloop Dataloop, an AI-driven data management platform, is tailored to streamline the process of generating data for AI. While Scale AI is recognized for its human-centric approach to data labeling, Dataloop differentiates itself with its cloud-based platform, providing flexibility and scalability for organizations of all sizes. ML Pipeline & Features Streamlines administrative tasks efficiently, organizing management and numerical data. Dataloop's object tracking and detection feature stands out, providing users with exceptional data quality Requires a stable and fast internet connection, which might pose challenges in areas with connectivity issues. Integration and Compatibility Dataloop, being a cloud-based platform, offers the advantage of flexibility. However, it also requires a stable and fast internet connection, which might pose challenges in areas with connectivity issues. Despite this, its integration capabilities ensure users can seamlessly connect their data sources and ML models to the platform. Benefits and Customer Feedback Dataloop has received positive feedback from its users. Users have noted the platform's scalability and flexibility, making it suitable for both small projects and larger needs. However, some users have pointed out that the user interface can be challenging to navigate, suggesting the need for tutorials or a more intuitive design. Here is the G2 link for customer reviews on the Dataloop platform. SuperAnnotate SuperAnnotate offers tools to streamline annotation. Their platform is equipped with tools and automation features that enable the creation of accurate training data across multiple data types. SuperAnnotate's offerings include the LLM Editor, Image Editor, Video Editor, Text Editor, LiDAR Editor, and Audio Editor. ML Pipeline &  Features Features like data insights, versioning, and a query system to filter and find relevant data Marketplace of over 400 annotation teams that speak 18 languages. This ensures high-quality annotations tailored to specific regional and linguistic requirements  Dedicated annotation project managers, ensuring stellar project delivery Annotation tools for different data types, from images and videos to LiDAR and audio Certifications like SOC 2 Type 2, ISO 27001, and HIPAA Data integrations with major cloud platforms like AWS, Azure, and GCP Benefits and Customer Feedback SuperAnnotate has received positive user feedback, with companies like Hinge Health praising the platform's high and consistent quality. Refer to the G2 link for customers' thoughts about the SuperAnnotate platform. Labelbox Labelbox, a leading data labeling platform, is designed to focus on collaboration and automation. It offers a centralized hub where teams can create, manage, and maintain high-quality training data. Labelbox provides tools for image, video, and text annotations. ML Pipeline & Features Labelbox supports data collection to model training Features include MAL (Model Assisted Labeling), which uses pre-trained models to accelerate the labeling process Easy collaboration, allowing multiple team members to work on the same dataset and ensuring annotation consistencyReviewer Workflow feature enables quality assurance by allowing senior team members to review and approve annotations Ontology Manager provides a centralized location to manage labeling instructions, ensuring clarity and consistency API integrations, allowing users to connect their data sources and ML models to the platform Supports integrations with popular cloud storage solutions Integration and Compatibility Labelbox offers API integrations, allowing users to connect their data sources and ML models seamlessly to the platform. This ensures a workflow from data ingestion to model training. The platform also supports integrations with popular cloud storage solutions, ensuring flexibility in data management. Here is the G2 link for customer reviews about the LabelBox platform. Scale Alternatives: Key Takeaways Scale’s interactive platform has been recognized for its excellent automation and streamlined workflows tailored for various use cases. While many platforms in the market are open-source, Scale AI's proposition lies in its focus on machine learning and AI-powered algorithms. The platform offers a range of plugins and tools that provide metrics and insights in real-time. With its robust API integrations, it seamlessly connects with platforms like Amazon, ensuring that artificial intelligence is leveraged to its full potential. The user-friendly interface of Scale AI, combined with its suite of AI tools, facilitates creating and managing datasets. This has made it a preferred choice for industries ranging from social media giants to tech behemoths like Microsoft. Scale AI's platform ensures seamless integration and functionality using Windows, iOS, or any other operating system. The semantic understanding and capabilities of platforms like GPT-3 have further underscored the importance of training data in sectors like healthcare. With companies like OpenAI launching tools like ChatGPT, the emphasis on NLP (Natural Language Processing) and computer vision has never been higher.  Platforms that are self-hosted, offer podcast transcription services, or focus on pixel-perfect data labeling are gaining traction. The rise of chatbots and tools that optimize customer support using GPT-4 and other advanced algorithms is reshaping the landscape. In this rapidly evolving domain, optimizing workflows and harnessing the power of natural language processing is paramount.  Here are our key takeaways: The AI domain is witnessing a transformative phase with new platforms and tools emerging. As industries seek efficient data labeling and management solutions, platforms like Encord are becoming indispensable. Encord's AI-assisted labeling accelerates the creation of high-quality training data, making it a prime choice in this evolving landscape. One of the standout features of modern AI platforms is the ability to harness AI for faster and more accurate data annotation. Encord excels in this with its AI-powered labeling, enabling users to annotate visual data swiftly and deploy models up to 10 times faster than traditional methods.

Oct 12 2023

sampleImage_sudoku-solver-cv-project

A Guide to Building a Sudoku Solver CV Project 

If you are a big fan of solving puzzles, you must have seen or played Sudoku at some point. Sudoku is one of the most beloved puzzle games worldwide. The game might appear as a simple puzzle of 9x9 boxes. However, it requires concentration and mental agility to complete. Additionally, the game might seem math-based, but you do not need to excel at mathematics to solve this puzzle game. Instead, Sudoku is a game of pattern recognition, and once you identify that, you have won! Number puzzles appeared in newspapers in the late 19th century, when French puzzle setters began experimenting with removing numbers from magic squares. Le Siècle, a Paris daily, published a partially completed 9×9 magic square with 3×3 subsquares on November 19, 1892. To make things fun, how about involving technology in this game? With the Sudoku Solver Project, we aim to make puzzle-solving even more interesting, lively, and competitive. You might be wondering how. The star of this project is OpenCV. By involving OpenCV, we will turn your computer into a smart brain that can easily solve Sudoku. Now, you might be thinking, “What is the tech behind it that would make your computer as smart as your brain in solving puzzles?” OpenCV is a library of various programming functions for computer vision tasks. Computer vision will give your device the ability to understand images. This is how your computer can decode and solve puzzle games like Sudoku. Equipped with OpenCV, computer vision can easily recognize lines, grids, numbers, boxes, etc. It will be possible for the device to detect and understand the patterns and solve them accordingly. Do you find it intriguing? Learn more about how OpenCV can help transform your computer into a puzzle-solving genius. Here’s an outline of the article: Project Overview Fusing OpenCV with Python: A Winning Combination Image Processing Disclosing Puzzle Numbers with Digit Recognition Grid Extraction to Decode Puzzle Structure Breaking Sudoku Code with Algorithm The Final Result: Puzzle Solved! Key Takeaways Project Overview The main aim of this project is quite simple yet ambitious. We want to teach machines how to understand and solve puzzles like Sudoku. Since solving Sudoku requires high analytical capabilities, the aim is to equip computers with the power of computer vision. Computer vision allows them to see a puzzle like a human brain.  With computer vision, your computer will become a pro at solving puzzles alone, without human intervention. Now, that would be super cool, isn't it? That is what our project aims for. Fusing OpenCV with Python to Solve Sudoku: A Winning Combination We have integrated two powerful tools for our Sudoku Solver Project: Python and OpenCV. Python is one of the most popular and widely used programming languages worldwide, primarily because of its simplicity and readability. With Python, developers can design and build complex applications without writing overly complicated code. OpenCV is a powerful library primarily used for real-time computer vision applications. It plays a pivotal role in model execution within Artificial Intelligence and Machine Learning. OpenCV provides a comprehensive set of tools that allow developers to train models to understand images and identify patterns, making it ideal for tasks like solving puzzles. By leveraging the capabilities of OpenCV and Python, this project aims to equip your computer to solve Sudoku puzzles autonomously. Observing a computer tackle these puzzles with precision will be an intriguing experience. Find the complete code for this project in this repository. Image Processing  One of the crucial parts of solving the puzzle would be preparing the image through image processing. The system needs a proper and usable puzzle image. The image needs to be cleaned up well and prepared accordingly. After all, our system will use this image for the final puzzle-solving job. Now, that involves multiple steps.  Let’s find out about each step in detail below:  Image Loading  The first step would be loading the image. Here, OpenCV will play a crucial role. OpenCV is a massive open-source library designed for computer vision and image processing. It is highly useful for image loading and image processing. It processes images and videos to identify faces, objects, grids, etc. Whenever the image needs to be loaded from a location specified by the path of the file, a particular function in OpenCV is required. With image loading, the image can be integrated into the program as either a grayscale or color image.     Image Resizing  Once the image gets loaded, we will ensure that the image is not too big for the computer. The image must be resized so our computer can process it better and faster. Image resizing matters in this regard, as the image has to be fitted to a format and size that allow the computer to process it efficiently.   Filter Application  We now need to apply a filter to enhance the clarity of the image. For this, we will use the Gaussian blur filter. This is a widely used technique in image processing and graphics software, where image blurring is done using the Gaussian function. It helps in reducing image noise and image detail. Applying Gaussian Blur distribution on a 3x3 kernel Placing Everything Together in Place Now that the puzzle image is processed, resized, and given a little touch-up with a filter, it is ready! All these steps are necessary to make the image properly visible and understandable to the computer’s vision. Once all this is completed, it is time to move on to the next big steps – digit recognition and grid extraction. While image processing is also an important step, it is the stepping stone to the showdown. These are two of the most crucial and indispensable procedures our computer needs to perform for Sudoku solving. Let’s dive into the details. Recognizing the Missing Digits  This is the next big step in converting your system to a smart puzzle-solver to make it unmask the digits in the image. Sudoku is all about numbers and identifying the pattern that lies in them. Thus, your computer needs to find and figure out the numbers first to process the underlying patterns. With the Sudoku Solver, we will enable computers to check out each cell and identify their number. Machine learning will play a vital role in this case. Let’s go find out more about digit recognition here:  Separating Individual Numbers  To recognize the digits, the computer must treat each cell separately. It will focus on one cell at a time to determine the number in each cell. For this, we will use a Digit Recognizer model.  Load the model from the file in the repository: new_model = tf.keras.models.load_model('Digit_Recognizer.h5') Using Machine Learning  The actual task begins when the model has separated the number from each cell. It has to know the number in each cell. Convolutional Neural Network (CNN) is the algorithm you will use to train the model to identify numbers by itself.   The broad approach of Artificial Intelligence (AI) is to replicate the rational abilities of humans in a computational environment. One of the best ways to evaluate the capabilities of an AI is to see if they can beat humans at playing games. The model inspects each cell and recognizes the numbers for the CNN module. This ML module then plays its magic of identifying what exactly the numbers are in the cell of the puzzle game. With image processing and a little ML module, your system turns into a smart puzzle-solver.  Convolutional Neural Network Grid Extraction to Decode Puzzle Structure After image processing and digit detection, the model must detect the puzzle grid. Detecting and then extracting the puzzle grid of the game will help the model detect where each cell starts and ends. Understanding the grid will let the device decode the puzzle structure. Here are the steps through which the puzzle grid is detected and extracted:  Decoding the Grid Lines The computer will begin by detecting the grid lines of the Sudoku puzzle. We will use a technique in OpenCV called contour detection to detect the grid lines in a Sudoku puzzle. Through contour detection, our model will learn to find the puzzle frames. With contour detection, we will detect the borders of any object and then localize those borders in an image. Contour is something we get by joining all the points on the boundary of an object. OpenCV is useful for finding and drawing contours in an image. OpenCV has two straightforward functions for contour detection. These are “findContours()” and “drawContours()”. There are multiple steps involved in contour detection. These steps include reading an image and converting it into a grayscale format, applying a binary threshold, detecting the contours using the function “findContours()” and drawing them on the original image.  The largest contours in the Sudoku puzzle are located in the corners. Contour detection with the help of OpenCV Grid Cell Extracting  Now that the model learns where each grid line of the puzzle is, it has to extract each puzzle cell. This process is breaking the puzzle into different tiny bits. It will help the model facilitate a finer inspection of every cell of the game.  The code below will smoothly decide and extract the grid lines chronologically: #To order predicted digit nested list def display_predList(predList): predicted_digits = [] for i in range(len(predList)): for j in range(len(predList)): predicted_digits.append(predList[j][i]) return predicted_digits #Parameters for Warping the image margin = 10 case = 28 + 2*margin perspective_size = 9*case cap = cv2.VideoCapture(0) flag = 0 ans = 0 while True: ret, frame=cap.read() p_frame = frame.copy() #Process the frame to find contour gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) gray=cv2.GaussianBlur(gray, (5, 5), 0) thresh=cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 9, 2) #Get all the contours in the frame contours_, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contour = None maxArea = 0 #Find the largest contour(Sudoku Grid) for c in contours_: area = cv2.contourArea(c) if area > 25000: peri = cv2.arcLength(c, True) polygon = cv2.approxPolyDP(c, 0.01*peri, True) if area>maxArea and len(polygon)==4: contour = polygon maxArea = area #Draw the contour and extract Sudoku Grid if contour is not None: cv2.drawContours(frame, [contour], 0, (0, 255, 0), 2) points = np.vstack(contour).squeeze() points = sorted(points, key=operator.itemgetter(1)) if points[0][0]<points[1][0]: if points[3][0]<points[2][0]: pts1 = np.float32([points[0], points[1], points[3], points[2]]) else: pts1 = np.float32([points[0], points[1], points[2], points[3]]) else: if points[3][0]<points[2][0]: pts1 = np.float32([points[1], points[0], points[3], points[2]]) else: pts1 = np.float32([points[1], points[0], points[2], points[3]]) pts2 = np.float32([[0, 0], [perspective_size, 0], [0, perspective_size], [perspective_size, perspective_size]]) matrix = cv2.getPerspectiveTransform(pts1, pts2) perspective_window =cv2.warpPerspective(p_frame, matrix, (perspective_size, perspective_size)) result = perspective_window.copy() #Process the extracted Sudoku Grid p_window = cv2.cvtColor(perspective_window, cv2.COLOR_BGR2GRAY) p_window = cv2.GaussianBlur(p_window, (5, 5), 0) p_window = cv2.adaptiveThreshold(p_window, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 9, 2) vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(5,5)) p_window = cv2.morphologyEx(p_window, cv2.MORPH_CLOSE, vertical_kernel) lines = cv2.HoughLinesP(p_window, 1, np.pi/180, 120, minLineLength=40, maxLineGap=10) for line in lines: x1, y1, x2, y2 = line[0] cv2.line(perspective_window, (x1, y1), (x2, y2), (0, 255, 0), 2) #Invert the grid for digit recognition invert = 255 - p_window invert_window = invert.copy() invert_window = invert_window /255 i = 0 #Check if the answer has been already predicted or not #If not predict the answer #Else only get the cell regions if flag != 1: predicted_digits = [] pixels_sum = [] #To get individual cells for y in range(9): predicted_line = [] for x in range(9): y2min = y*case+margin y2max = (y+1)*case-margin x2min = x*case+margin x2max = (x+1)*case-margin #Obtained Cell image = invert_window[y2min:y2max, x2min:x2max] #Process the cell to feed it into model img = cv2.resize(image,(28,28)) img = img.reshape((1,28,28,1)) #Get sum of all the pixels in the cell #If sum value is large it means the cell is blank pixel_sum = np.sum(img) pixels_sum.append(pixel_sum) #Predict the digit in the cell pred = new_model.predict(img) predicted_digit = pred.argmax() #For blank cells set predicted digit to 0 if pixel_sum > 775.0: predicted_digit = 0 #If we already have predicted result, display it on window if flag == 1: ans = 1 x_pos = int((x2min + x2max)/ 2)+10 y_pos = int((y2min + y2max)/ 2)-5 image = cv2.putText(result, str(pred_digits[i]), (y_pos, x_pos), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA) i = i + 1 #Get predicted digit list if flag != 1: predicted_digits.append(predicted_line) Placing Everything Together  Once grid line detection with contour detection and grid cell extraction are complete, the model is ready to dissect the entire puzzle. This step also requires image cropping and image warping. To crop the image, you need to know the dimensions of the Sudoku image. A Sudoku puzzle is usually square and comes with equal dimensions.  We need to measure and calculate the height and width to ensure that we do not accidentally crop out any part of the puzzle piece. After constructing the dimension, we will get the grid and then warp the image. The codes required for image warping are provided above. These processes let the device dissect into each puzzle cell. Let us now move on to the next vital step.  Breaking Sudoku Code with Algorithm This is the final step. Our model would be unwrapping the Sudoku puzzle and solving it. The device has identified the numbers and detected the grid lines, and now it is time to unveil and crack the puzzle. For this final task, we will use an algorithm called backtracking. Backtracking is a handy tool used for solving constraint satisfaction problems such as puzzle solving, Sudoku, crosswords, verbal mathematics and more. With backtracking, we will make the final move to solve the Sudoku puzzle.   Backtracking is a depth-first search (in contrast to a breadth-first search), because it will completely explore one branch to a possible solution before moving to another branch. Implementing the Backtracking Algorithm With backtracking, our model will be solving the Sudoku puzzle. Backtracking algorithm searches every possible combination to find the solution to a computational problem. The system will test every cell to find the one that fits. Backtracking is a systematic, algorithmic approach that explores possible solutions by making choices and recursively exploring them until a solution is found or deemed impossible.  It begins with an initial choice and proceeds step by step, backtracking or undoing choices and trying alternatives whenever it encounters an invalid or unsatisfactory solution path. This method is particularly effective for solving puzzles with complex and branching solution spaces, such as Sudoku, the Eight-Puzzle, or the N-Queens problem, where constraints or rules must be satisfied and an exhaustive search is necessary to find the correct solution. Backtracking ensures that all possibilities are explored while minimizing memory usage and providing a deterministic way to find solutions. Backtracking Applying the Sudoku Rules  Sudoku comes with unique rules, like any other puzzle game. The model needs to follow these rules while solving the game. It will place the numbers in a cell only after ensuring it abides by the rules of Sudoku.  You can now observe the model's meticulous process as it navigates through each cell, methodically unraveling the puzzle. By amalgamating all the steps and techniques previously outlined, the model actively engages in the quest for a solution. It diligently applies the rules of Sudoku, emulating the problem-solving strategies of the human brain in its relentless pursuit of resolving the puzzle. The Final Result: Puzzle Solved! When equipped with computer vision and the below code, our model becomes nothing less than a brilliant puzzle-solving champion, and when it finally solves the puzzle, you will be amazed at its accuracy and efficiency. Below is the complete code for getting the final Sudoku result. Also, find it in this repository.  #Get solved Sudoku ans = solveSudoku(predicted_digits) if ans==True: flag = 1 pred_digits = display_predList(predicted_digits) To display the final result of the puzzle, use:  #Display the final result if ans == 1: cv2.imshow("Result", result) frame = cv2.warpPerspective(result, matrix, (perspective_size, perspective_size), flags=cv2.WARP_INVERSE_MAP) cv2.imshow("frame", frame) cv2.imshow('P-Window', p_window) cv2.imshow('Invert', invert) cv2.imshow("frame", frame) key=cv2.waitKey(1)&0xFF if key==ord('q'): break cap.release() cv2.destroyAllWindows() Computer vision actually goes beyond just solving Sudoku. It can solve multiple real-world problems and play various games. Computer vision is a great option to find solutions to many issues. OpenCV, Python, and some Machine Learning can be a winning combination for allowing computers to solve puzzles like human brains.  The Sudoku Solver CV Project is not just about allowing computers to solve puzzles like humans. It is about the sheer thrill, joy, and excitement of blending technology with the fantastic capabilities of the human brain to achieve real-world solutions. Key Takeaways Here are the major points that we find here: You can turn your computer into a puzzle-solving genius just like a human mind with the Sudoku Solver CV Project. The Sudoku Solver CV Project uses OpenCV, Machine Learning and backtracking algorithms to solve Sudoku puzzles faster and more efficiently. There are three main steps involved in making a computer a Sudoku-solving wizard. These include image loading, resizing and processing, digit recognition, and grid cell extraction.  OpenCV plays a crucial role in image loading, resizing, and processing. Convolutional Neural Network, a Machine Learning algorithm, is crucial for digit recognition. It teaches the computer to detect a digit in any cell without human intervention. Contour Detection is a significant method used in grid cell detection. This technique allows the system to find the borders of any image. This technique is essential to understanding the borderline of the grid and is crucial for extracting grid cells. Backtracking is a practical algorithm necessary to solve the puzzle because it systematically explores solution spaces while respecting constraints, making it suitable for a wide range of complex problems. 

Oct 05 2023

sampleImage_openai-dall-e-3-what-we-know-so-far

OpenAI’s DALL-E 3 Explained: Generate Images with ChatGPT

In the field of image generation, OpenAI continues to push the boundaries of what’s possible. On September 20th, 2023 Sam Altman announced DALL-E 3, which is set to revolutionize the world of text-to-image generation.  Fueled by Microsoft's support, the firm is strategically harnessing ChatGPT's surging popularity to maintain its leadership in generative AI, a critical move given the escalating competition from industry titans like Google and emerging disruptors like Bard, Midjourney, and Stability AI. DALL-E 3: What We Know So Far DALL-E 3 is a text-to-image model which is built upon DALL-E 2 and ChatGPT. It excels in understanding and translating textual descriptions into highly detailed and accurate images.   Watch the demo video for DALL-E 3!   While this powerful AI model is still in research preview, there's already a lot to be excited about. Here's a glimpse into what we know so far about DALL-E 3: Eliminating Prompt Engineering DALL-E 3 is set to redefine how we think about generating images from text. Modern text-to-image systems often fall short by ignoring words or descriptions, thereby requiring users to master the art of prompt engineering. In contrast, DALL·E 3 represents a remarkable leap forward in our ability to generate images that precisely adhere to the text provided, eliminating the complexities of prompt engineering. Integrated seamlessly with ChatGPT, DALL·E 3 acts as a creative partner, allowing users to effortlessly bring their ideas to life by generating tailored and visually stunning images from simple sentences to detailed paragraphs. DALL-E 3 Improved Precision DALL-E 3 is set to redefine how we think about generating images from text prompts. Previously DALL-E, like other generative AI models has shown issues interpreting complex text prompts and often mixing two concepts while generating images. Unlike its predecessors, this model is designed to understand text prompts with remarkable precision, capturing nuance and detail like never before. Focus on Ethical AI OpenAI is acutely aware of the ethical considerations that come with image generation models. To address these concerns, DALL-E 3 incorporates safety measures that restrict the generation of violent, adult, or hateful content. Moreover, it has mitigations in place to avoid generating images of public figures by name, thereby safeguarding privacy and reducing the risk of misinformation. OpenAI's commitment to ethical AI is further underscored by its collaboration with red teamers and domain experts. These partnerships aim to rigorously test the model and identify and mitigate potential biases, ensuring that DALL-E 3 is a responsible and reliable tool. Just this week, OpenAI unveiled the "OpenAI Red Teaming Network," a program designed to seek out experts across diverse domains. The aim is to engage these experts in evaluating their AI models, thereby contributing to the informed assessment of risks and the implementation of mitigation strategies throughout the entire lifecycle of model and product development. Transparency  As AI-generated content becomes more prevalent, the need for transparency in identifying such content grows. OpenAI is actively researching ways to help people distinguish AI-generated images from those created by humans. They are experimenting with a provenance classifier, an internal tool designed to determine whether an image was generated by DALL-E 3. This initiative reflects OpenAI's dedication to transparency and responsible AI usage. DALL-E 3 This latest iteration of DALL-E is scheduled for an initial release in early October, starting with ChatGPT Plus and ChatGPT Enterprise customers, with subsequent availability in research labs and through its API service in the autumn. OpenAI intends to roll out DALL-E 3 in phases but has not yet confirmed a specific date for a free public release. When DALL-E 3 is launched, you'll discover an in-depth explanation article about it on Encord! Stay tuned! Recommended Topics for Pre-Release Reading To brace yourself for the release and help you dive right into it, here are some suggested topics you can explore: Transformers Transformers are foundational architectures in the field of artificial intelligence, revolutionizing the way machines process and understand sequential data. Unlike traditional models that operate sequentially, Transformers employ parallel processing, making them exceptionally efficient. They use mechanisms like attention to weigh the importance of different elements in a sequence, enabling tasks such as language translation, sentiment analysis, and image generation. Transformers have become the cornerstone of modern AI, underpinning advanced models like DALL-E, ChatGPT, etc. For more information about Vision Transformers read Introduction to Vision Transformers (ViT)  Foundation Models Foundation models are the bedrock of contemporary artificial intelligence, representing a transformative breakthrough in machine learning. These models are pre-trained on vast datasets, equipping them with a broad understanding of language and knowledge. GPT-3 and DALL-E, for instance, are prominent foundation models developed by OpenAI. These models serve as versatile building blocks upon which more specialized AI systems can be constructed. After pre-training on extensive text data from the internet, they can be fine-tuned for specific tasks, including natural language understanding, text generation, and even text-to-image conversion, as seen in DALL-E 3. Their ability to generalize knowledge and adapt to diverse applications underscores their significance in AI's rapid advancement. Foundation models have become instrumental in numerous fields, including large language models, AI chatbots, content generation, and more. Their capacity to grasp context, generate coherent responses, and perform diverse language-related tasks makes them invaluable tools for developers and researchers. Moreover, the flexibility of foundation models opens doors to creative and practical applications across various industries. For more information about foundation models read The Full Guide to Foundation Models  Text-to-Image Generation Text-to-image generation is a cutting-edge field in artificial intelligence that bridges the gap between textual descriptions and visual content creation. In this remarkable domain, AI models use neural networks to translate written text into vivid, pixel-perfect images. These models understand and interpret textual input, capturing intricate details, colors, and context to produce striking visual representations. Text-to-image generation finds applications in art, design, content creation, and more, offering a powerful tool for bringing creative ideas to life. As AI in this field continues to advance, it holds the promise of revolutionizing how we communicate and create visual content, offering exciting possibilities for artists, designers, and storytellers. Read the paper Zero-Shot Text-to-Image Generation by A. Ramesh, et al from OpenAI to understand how DALL-E generates images!  

Sep 21 2023

sampleImage_gpt-vision-vs-gemini-expectations

With Google gearing up to release Gemini this fall set to rival OpenAI’s GPT-Vision, it is going to be the Oppenheimer vs. Barbie of generative AI.  OpenAI and Google have been teasing their ground-breaking advancements in multimodal learning. Let's discuss what we know so far. Google’s Gemini: What We Know So Far At the May 2023 Google I/O developer conference, CEO Sundar Pichai unveiled Google's upcoming artificial intelligence (AI) system, codenamed Gemini. Developed by the esteemed DeepMind division, a collaboration between the Brain Team and DeepMind itself, Gemini represents a groundbreaking advancement in AI.  While detailed information remains confidential, recent interviews and reports have provided intriguing insights into the power and potential of Google's Gemini. Interested in fine-tuning foundation models, contact sales to discuss your use case.   Gemini’s Multimodal Integration Google CEO Sundar Pichai emphasized that Gemini combines DeepMind's AlphaGo strengths with extensive language modeling capabilities. With a multimodal design, Gemini seamlessly integrates text, images, and other data types, enabling more natural conversational abilities. Pichai also hinted at the potential for memory and planning features, which opens doors for tasks requiring advanced reasoning. Diverse Sizes and Capabilities Demis Hassabis, the CEO of DeepMind, provides insight into the versatility of Gemini. Drawing inspiration from AlphaGo's techniques such as reinforcement learning and tree search, Gemini is poised to acquire reasoning and problem-solving abilities. This "series of models" will be available in various sizes and capabilities, making it adaptable to a wide range of applications. Enhancing Accuracy and Content Quality Hassabis suggested that Gemini may employ techniques like fact-checking against sources such as Google Search and improved reinforcement learning. These measures are aimed at ensuring higher accuracy and reducing the generation of problematic or inaccurate content. Universal Personal Assistant In a recent interview, Sundar Pichai discussed Gemini's place in Google's product roadmap. He made it clear that conversational AI systems like Bard represent mere waypoints, not the ultimate goal. Pichai envisions Gemini and its future iterations as "incredible universal personal assistants," seamlessly integrated into people's daily lives, spanning various domains such as travel, work, and entertainment. He even suggests that today's chatbots will appear "trivial" compared to Gemini's capabilities within a few years. GPT-Vision: What We Know So Far OpenAI recently introduced GPT-4, a multimodal model that has the ability to process both textual and visual inputs, and in turn, generate text-based outputs. GPT-4, which was unveiled in March, was initially made available to the public through a subscription-based API with limited usage. It is speculated that the full potential of GPT-4 will be revealed in the autumn as GPT-Vision, coinciding with the launch of Google’s Gemini. GPT-4 Technical Report According to the paper published by OpenAI, the following is the current information available on GPT-Vision: Transformer-Based Architecture At its core, GPT-Vision utilizes a Transformer-based architecture that is pre-trained to predict the next token in a document, similar to its predecessors. Post-training alignment processes have further improved the model's performance, particularly in terms of factuality and adherence to desired behavior. Human-Level Performance GPT-4's capabilities are exemplified by its human-level performance on a range of professional and academic assessments. For instance, it achieves remarkable success in a simulated bar exam, with scores that rank among the top 10% of test takers. This accomplishment marks a significant improvement over its predecessor, GPT-3.5, which scored in the bottom 10% on the same test. GPT-Vision is expected to show similar performance if not better. Reliable Scaling and Infrastructure A crucial aspect of GPT-4's development involved establishing robust infrastructure and optimization methods that behave predictably across a wide range of scales. This predictability allowed us to accurately anticipate certain aspects of GPT-Vision's performance, even based on models trained with a mere fraction of the computational resources. Test-Time Techniques GPT-4 effectively leverages well-established test-time techniques developed for language models, such as few-shot prompting and chain-of-thought. These techniques enhance its adaptability and performance when handling both images and text. GPT-4 Technical Report Recommended Pre-release Reading Multimodal Learning Multimodal learning is a fascinating field within artificial intelligence that focuses on training models to understand and generate content across multiple modalities. These modalities encompass text, images, audio, and more. The main goal of multimodal learning is to empower AI systems to comprehend and generate information from various sensory inputs simultaneously. Multimodal learning demonstrates tremendous potential across numerous domains, including natural language processing, computer vision, speech recognition, and other areas where information is presented in diverse formats. Interested in multimodal learning? Read Introduction to Multimodal Deep Learning   Generative AI Generative AI refers to the development of algorithms and models that have the capacity to generate new content, such as text, images, music, or even video, based on patterns and data they've learned during training. These models are not only fascinating but also incredibly powerful, as they have the ability to create content that closely resembles human-produced work. Generative AI encompasses a range of techniques, including generative adversarial networks (GANs), autoencoders, and transformer-based models. It has wide-ranging applications, from creative content generation to data augmentation and synthesis. Transformers Transformers are a class of neural network architectures that have significantly reshaped the field of deep learning. Introduced in the landmark paper "Attention Is All You Need" by Vaswani et al. in 2017, Transformers excel at processing sequential data. They employ self-attention mechanisms to capture relationships and dependencies between elements in a sequence, making them highly adaptable for various tasks. Transformers have revolutionized natural language processing, enabling state-of-the-art performance in tasks like machine translation and text generation. Their versatility extends to other domains, including computer vision, audio processing, and reinforcement learning, making them a cornerstone in modern AI research. Interested in Vision Transformers? Read Introduction to Vision Transformers (ViT)  Future Advancements in Multimodal Learning Recent Advances and Trends in Multimodal Deep Learning: A Review  Multimodal Image Description Enhanced language generation models for accurate and grammatically correct captions. Advanced attention-based image captioning mechanisms. Incorporation of external knowledge for context-aware image descriptions. Multimodal models for auto video subtitling. Multimodal Video Description Advancements in video dialogue systems for human-like interactions with AI. Exploration of audio feature extraction to improve video description in the absence of visual cues. Leveraging real-world event data for more accurate video descriptions. Research on combining video description with machine translation for efficient subtitling. Focus on making video subtitling processes cost-effective. Multimodal Visual Question Answering (VQA) Design of goal-oriented datasets to support real-time applications and specific use cases. Exploration of evaluation methods for open-ended VQA frameworks. Integration of context or linguistic information to enhance VQA performance. Adoption of context-aware image feature extraction techniques. Multimodal Speech Synthesis Enhancement of data efficiency for training End-to-End (E2E) DLTTS (Deep Learning Text-to-Speech) models. Utilization of specific context or linguistic information to bridge the gap between text and speech synthesis. Implementation of parallelization techniques to improve efficiency in DLTTS models. Integration of unpaired text and speech recordings for data-efficient training. Exploration of new feature learning techniques to address the "curse of dimensionality" in DLTTS. Research on the application of speech synthesis for voice conversion, translation, and cross-lingual speech conversion. Multimodal Emotion Recognition Development of advanced modeling and recognition techniques for non-invasive emotion analysis. Expansion of multimodal emotion recognition datasets for better representation. Investigation into the preprocessing of complex physiological signals for emotion detection. Research on the application of automated emotion recognition in real-world scenarios. Multimodal Event Detection Advancements in feature learning techniques to address the "curse of dimensionality" issue. Integration of textual data with audio and video media for comprehensive event detection. Synthesizing information from multiple social platforms using transfer learning strategies. Development of event detection models that consider real-time applications and user interactions. Designing goal-oriented datasets for event detection in specific domains and applications. Exploration of new evaluation methods for open-ended event detection frameworks.

Sep 20 2023

sampleImage_multimodal-learning-guide

Humans perceive the world using the five senses (vision, hearing, taste, smell, and touch). Our brain uses a combination of two, three, or all five senses to perform conscious intellectual activities like reading, thinking, and reasoning. These are our sensory modalities. In computing terminology, the equivalent of these senses are various data modalities, like text, images, audio, and videos, which are the basis for building intelligent systems. If artificial intelligence (AI) is to truly imitate human intelligence, it needs to combine multiple modalities to solve a problem.  Multimodal learning is a multi-disciplinary approach that can handle the heterogeneity of data sources to build computer agents with intelligent capabilities.  This article will introduce multimodal learning, discuss its implementation, and list some prominent use cases. We will discuss popular multimodal learning techniques, applications, and relevant datasets. What is Multimodal Learning in Deep Learning? Multimodal deep learning trains AI models that combine information from several types of data simultaneously to learn their unified data representations and provide contextualized results with higher predictive accuracy for complex AI tasks. Today, modern AI architectures can learn cross-modal relationships and semantics from diverse data types to solve problems like image captioning, image and text-based document classification, multi-sensor object recognition, autonomous driving, video summarization, multimodal sentiment analysis, etc. For instance, in multimodal autonomous driving, AI models can process data from multiple input sensors and cameras to improve vehicle navigation and maneuverability. The Significance of Multimodal Data in the Real World Real-world objects generate data in multiple formats and structures, such as text, image, audio, video, etc. For example, when identifying a bird, we start by looking at the creature itself (visual information). Our understanding grows if it’s sitting on a tree (context). The identification is further solidified if we hear the bird chirping (audio input). Our brain can process this real-world information and quickly identify relationships between sensory inputs to generate an outcome. However, present-day machine learning models are nowhere as complex and intricate as the human brain. Hence, one of the biggest challenges in building multimodal deep learning models is processing different input modalities simultaneously.  Each data type has a different representation. For example, images consist of pixels, textual data is represented as a set of characters or words, and audio is represented using sound waves. Hence, a multimodal learning architecture requires specialized data transformations or representations for fusing multiple inputs and a complex deep network to understand patterns from the multifaceted training data. Let’s talk more about how a multimodal model is built. 🔥 NEW RELEASE: We released TTI-Eval (text-to-image evaluation), an open-source library for evaluating zero-shot classification models like CLIP and domain-specific ones like BioCLIP against your (or HF) datasets to estimate how well the model will perform. Get started with it on GitHub, and do ⭐️ the repo if it's awesome. 🔥 Dissecting Multimodal Machine Learning Although the multimodal learning approach has only become popular recently, there have been few experiments in the past. Srivastava and Salakhutdinov demonstrated multimodal learning with Deep Boltzmann Machines back in 2012. Their network created representations or embeddings for images and text data and fused the layers to create a single model that was tested for classification and retrieval tasks. Although the approach was not popular at the time, it formed the basis of many modern architectures. Modern state-of-the-art (SOTA) multimodal architectures consist of distinct components that transform data into a unified or common representation.  Let’s talk about such components in more detail. How Multimodal Learning Works in Deep Learning? The first step in any deep learning project is to transform raw data into a format understood by the model. While this is easier for numerical data, which can be fed directly to the model, other data modalities, like text, must be transformed into word embeddings, i.e., similar words are represented as real-valued numerical vectors that the model can process easily.  With multimodal data, the various modalities have to be individually processed to generate embeddings and then fused. The final representation is an amalgamation of the information from all data modalities. During the training phase, multimodal AI models use this representation to learn the relationship and predict the outcomes for relevant AI tasks. There are multiple ways to generate embeddings for multimodal data. Let’s talk about these in detail. Input Embeddings The traditional method of generating data embeddings uses unimodal encoders to map data to a relevant space. This approach uses embedding techniques like Word2Vec for natural language processing tasks and Convolutional Neural Networks (CNNs) to encode images. These individual encodings are passed via a fusion module to form an aggregation of the original information, which is then fed to the prediction model. Hence, understanding each modality individually requires algorithms that function differently. Also, they need a lot of computational power to learn representations separately. Today, many state-of-the-art architectures utilize specialized embeddings designed to handle multimodal data and create a singular representation. These embeddings include Data2vec 2.0: The original Data2vec model was proposed by Meta AI’s Baevski, Hsu, et al. They proposed a self-supervised embedding model that can handle multiple modalities of speech, vision, and text. It uses the regular encoder-decoder architecture combined with a student-teacher approach. The student-encoder learns to predict masked data points while the teacher is exposed to the entire data. In December 2022, Meta AI proposed version 2.0 for the original framework, providing the same accuracy but 16x better performance in terms of speed. JAMIE: The Joint Variational Autoencoder for MultiModal Imputations and Embeddings is an open-source framework for embedding molecular structures. JAMIE solves the challenge of generating multi-modal data by taking partially matched samples across different cellular modalities. The information missing from certain samples is imputed by learning similar representations from other samples. ImageBind: ImageBind is a breakthrough model from Meta that can simultaneously fuse information from six modalities. It processes image and video data with added information such as text descriptions, color depth, and audio input from the image scene. It binds the entire sensory experience for the model by generating a single embedding consisting of contextual information from all six modalities. VilBERT: The Vision-and-Language BERT model is an upgrade over the original BERT architecture. The model consists of two parallel streams to process the two modalities (text and image) individually. The two streams interact via a co-attention transformer layer, i.e., one encoder transformer block for generating visual embeddings and another for linguistic embeddings. While these techniques can process multimodal data, each data modality usually creates an individual embedding that must be combined through a fusion module. If you want to learn more about embeddings, read our detailed blog on The Full Guide to Embeddings in Machine Learning. Fusion Module After feature extraction (or generating embeddings), the next step in a multimodal learning pipeline is multimodal fusion. This step combines the embeddings of different modalities into a single representation. Fusion can be achieved with simple operations such as concatenation or summation of the weights of the unimodal embeddings. However, the simpler approaches do not yield appreciable results. Advanced architectures use complex modules like the cross-attention transformer. With its attention mechanism, the transformer module has the advantage of selecting relevant modalities at each step of the process. Regardless of the approach, the optimal selection of the fusion method is an iterative process. Different approaches can work better in different cases depending on the problem and data type. Early, Intermediate, & Late Fusion Another key aspect of the multimodal architecture design is deciding between early, intermediate, and late fusion. Early fusion combines data from various modalities early on in the training pipeline. The single modalities are processed individually for feature extraction and then fused together. Intermediate fusion, also known as feature-level fusion, concatenates the feature representations from each modality before making predictions. This enables joint or shared representation learning for the AI model, resulting in improved performance. Late fusion processes each modality through the model independently and returns individual outputs. The independent predictions are then fused at a later stage using averaging or voting. This technique is less computationally expensive than early fusion but does not capture the relationships between the various modalities effectively. Popular Multimodal Datasets Piano Skills Assessment Dataset Sample A multimodal dataset consists of multiple data types, such as text, speech, and image. Some datasets may contain multiple input modalities, such as images or videos and their background sounds or textual descriptions. Others may contain different modalities in the input and output space, such as images (input) and their text captions (output) for image captioning tasks. Some popular multimodal datasets include: LJ Speech Dataset: A dataset containing public domain speeches published between 1884 and 1964 and their respective 13,100 short audio clips. The audios were recorded between 2016-17 and have a total length of 24 hours. The LJ Speech dataset can be used for audio transcription tasks or speech recognition. HowTo100M: A dataset consisting of 136M narrated video clips sourced from 1.2M YouTube videos and their related text descriptions (subtitles). The descriptions cover over 23K activities or domains, such as education, health, handcrafting, cooking, etc. This dataset is more suitable for building video captioning models or video localization tasks. MultiModal PISA: Introduced in the Piano Skills Assessment paper, the MultiModal PISA dataset consists of images of the piano being played and relevant annotations regarding the pianist’s skill level and tune difficulty. It also contains processed audio and videos of 61 piano performances. It is suitable for audio-video classification and skill assessment tasks. LAION 400K: A dataset containing 413M Image-Text pairs extracted from the Common Crawl web data dump. The dataset contains images with 256, 512, and 1024 dimensions, and images are filtered using OpenAI’s CLIP. The dataset also contains a KNN index that clusters similar images to extract specialized datasets. Popular Multimodal Deep Learning Models Many popular multimodal architectures have provided ground-breaking results in tasks like sentiment analysis, visual question-answering, and text-to-image generation. Let’s discuss some popular model architectures that are used with multimodal datasets. Stable Diffusion Stable Diffusion (SD) is a widely popular open-source text-to-image model developed by Stability AI. It is categorized under a class of generative models called Diffusion Models.  The model consists of a pre-trained Variational AutoEncoder (VAE) combined with a U-Net architecture based on a cross-attention mechanism to handle various input modalities (text and images). The encoder block of the VAE transforms the input image from pixel space to a latent representation, which downsamples the image to reduce its complexity. The image is denoised using the U-Net architecture iteratively to reverse the diffusion steps and reconstruct a sharp image using the VAE decoder block, as illustrated in the image below.  Stable Diffusion Architecture SD can create realistic visuals using short input prompts. For instance, if a user asks the model to create “A painting of the last supper by Picasso”, the model would create the following image or similar variations. Image Created By Stable Diffusion Using Input Prompt “A painting of the last supper by Picasso.” Or if the user enters the following input prompt: “A sunset over a mountain range, vector image.” The SD model would create the following image. Image Created By Stable Diffusion Using Input Prompt “A sunset over a mountain range, vector image.” Since SD is an open-source model, multiple variations of the SD architecture exist with different sizes and performances that fit different use cases. If you want to learn more about diffusion models, read our detailed blog on An Introduction to Diffusion Models for Machine Learning. Flamingo Flamingo is a few-shot learning Visual Language Model (VLM) developed by DeepMind. It can perform various image and video understanding tasks such as scene description, scene understanding QA, visual dialog, meme classification, action classification, etc. Since the model supports few-shot learning, it can adapt to various tasks by learning from a few task-specific input-output samples.  The model consists of blocks of a pre-trained NFNet-F6 Vision Encoder that outputs a flattened 1D image representation. The 1D representation is passed to a Perceiver Resampler that maps these features to a fixed number of output visual tokens, as illustrated in the image below. The Flamingo model comes in three size variants: Flamingo-3B, Flamingo-9B, and Flamingo-80B, and displays ground-breaking performance compared to similar SOTA models. Overview of Flamingo Architecture Meshed-Memory Transformer The Meshed-Memory Transformer is an image captioning model based on encoder-decoder architecture. The architecture comprises memory-augmented encoding layers responsible for processing multi-level visual information and a meshed decoding layer for generating text tokens. The proposed model produced state-of-the-art results, topping the MS-COCO online leaderboard and beating SOTA models, including Up-Down and RFNet. Architecture of Meshed Memory Transformer If you want to learn more about multimodal learning architectures, read our detailed blog on Meta-Transformer: Framework for Multimodal Learning. Applications of Multimodal Learning Multimodal deep neural networks have several prominent industry applications by automating media generation and analysis tasks. Let’s discuss some of them below. Image Captioning Image captioning is an AI model’s ability to comprehend visual information in an image and describe it in textual form. Such models are trained on image and text data and usually consist of an encoder-decoder infrastructure. The encoder processes the image to generate an intermediate representation, and the decoder maps this representation to the relevant text tokens. Social media platforms use image captioning models to segregate images into categories and similar clusters. One notable benefit of image captioning models is that people with visual impairment can use them to generate descriptions of images and scenes. This technology becomes even more crucial considering the 4.8 billion people in the world who use social media, as it promotes accessibility and inclusivity across digital platforms Results of an Image Captioning Model Image Retrieval Multimodal learning models can combine computer vision and NLP to link text descriptions to respective images. This ability helps with image retrieval in large databases, where users can input text prompts and retrieve matching images. For instance, OpenAI’s CLIP model provides a wide variety of image classification tasks using natural language text available on the internet. As a real-world example, many modern smartphones provide this feature where users can type prompts like “Trees” or “Landscape” to pull up matching images from the gallery.  Visual Question Answering (VQA) Visual QA improves upon the image captioning models and allows the model to learn additional details regarding an image or scenario. Instead of generating a single description, the model can answer questions regarding the image iteratively. VQA has several helpful applications, such as allowing doctors to better understand medical scans via cross-questioning or as a virtual instructor to enable visual learning process for students. Text-to-Image Models Image generation from text prompts is a popular generative AI application that has already found several use cases in the real world. Models like DALL.E 2, Stable Diffusion, and Midjourney can generate excellent images from carefully curated text prompts. Social media creators, influencers, and marketers are extensively utilizing text-to-image models to generate unique and royalty-free visuals for their content. These models have enhanced the speed and efficiency of the content and art generation process. Today, digital artists can create highly accurate visuals within seconds instead of hours. Images Generated Using Stable Diffusion Using Various Input Prompts 🔥 NEW RELEASE: We released TTI-Eval (text-to-image evaluation), an open-source library for evaluating zero-shot classification models like CLIP and domain-specific ones like BioCLIP against your (or HF) datasets to estimate how well the model will perform. Get started with it on GitHub, and do ⭐️ the repo if it's awesome. 🔥 Text-to-Sound Generation Text-to-sound generation models can be categorized into speech and music synthesis. While the former can create a human speech that dictates the input text prompt, the latter understands the prompt as a descriptor and generates a musical tune. Both auditory models work on similar principles but have distinctly different applications. Speech synthesis is already used to generate audio for social media video content. It can also help people with speech impairment. Moreover, artists are using text-to-sound models for AI music generation. They can generate music snippets quickly to add to their creative projects or create complete songs. For instance, an anonymous artist named Ghostwriter977 on Twitter recently submitted his AI-generated track “Heart on My Sleeve” for Grammy awards. The song sparked controversy for resembling the creative work of two real artists, Drake and The Weeknd. Overall, such models can speed up the content generation process significantly and improve the time to market for various creative projects. Emotion Recognition A multimodal emotion recognition AI model grasps various audiovisual cues and contextual information to categorize a person’s emotions. These models analyze features like facial expressions, body language, voice tone, spoken words, and any other contextual information, such as the description of any event. All this knowledge helps the model understand the subject’s emotions and categorize them accordingly. Emotion recognition has several key applications, such as identifying anxiety and depression in patients, conducting customer analysis, and recognizing whether a customer is enjoying the product. Furthermore, it can also be a key component for building empathetic AI robots, helping them understand human emotions and take necessary action. Different Emotions of Speakers in a Dialogue Multimodal Learning: Challenges & Future Research While we have seen many breakthroughs in multimodal learning, it is still nascent. Several challenges remain to be solved. Some of these key challenges are: Training time: Conventional deep learning models are already computationally expensive and take several hours to train. With multimodal, the model complexity is taken up a notch with various data types and fusion techniques. Reportedly, it can take up to 13 days to train a Stable Diffusion model using 256 A100 GPUs. Future research will primarily focus on generating efficient models that require less training and lower costs. Optimal Fusion Techniques: Selecting the correct fusion technique is iterative and time-consuming. Many popular techniques cannot capture modality-specific information and fully replicate the complex relationships between the various modalities. Researchers are creating advanced fusion techniques to comprehend the complexity of multimodal data. Interpretability: Lack of interpretation plagues all deep learning models. With multiple complex hidden layers capturing data from various modalities, the confusion only grows. Explaining how a model can comprehend various modalities and generate accurate results is challenging. Though researchers have developed various explainable multimodal techniques, numerous open challenges exist, such as insufficient evaluation metrics, lack of ground truth, and generalizability issues that must be addressed to apply multimodal AI in critical scenarios. Multimodal Learning: Key Takeaways Multimodal deep learning brings AI closer to human-like behavior by processing various modalities simultaneously. AI models can generate more accurate outcomes by integrating relevant contextual information from various data sources (text, audio, image). A multimodal model requires specialized embeddings and fusion modules to create representations of the different modalities. As multimodal learning gains traction, many specialized datasets and model architectures are being introduced. Notable multimodal learning models include Flamingo and Stable Diffusion. Multimodal learning has various practical applications, including text-to-image generation, emotion recognition, and image captioning. This AI field has yet to overcome certain challenges, such as building simple yet effective architectures to reduce training times and improve accuracy.

Sep 19 2023

sampleImage_what-is-out-of-distribution-ood-detection

What is Out-of-Distribution (OOD) Detection?

Imagine teaching a child about animals using only a book on farm animals. Now, what happens when this child encounters a picture of a lion or a penguin? Confusion, right? In the realm of deep neural networks, there's a similar story unfolding. It's called the closed-world assumption. Deep within the intricate layers of neural networks, there's a foundational belief we often overlook: the network will only ever meet data it's familiar with, data it was trained on. The true challenge isn't just about recognizing cows or chickens. It's about understanding the unfamiliar, the unexpected. It's about the lion in a world of farm animals. The real essence? The test data distribution. The test data should mirror the training data distribution for a machine learning model to perform optimally. However, in real-world scenarios, this is only sometimes the case. This divergence can lead to significant challenges, emphasizing the importance of detecting out-of-distribution (OOD) data.  As we delve deeper, we'll explore the intricacies of OOD detection and its pivotal role in ensuring the robustness and reliability of artificial intelligence systems. Out of Distribution Samples The Importance of OOD Detection Out-of-Distribution (OOD) detection refers to a model's ability to recognize and appropriately handle data that deviates significantly from its training set.  The closed-world assumption rests on believing that a neural network will predominantly encounter data that mirrors its training set. But in the vast and unpredictable landscape of real-world data, what happens when it stumbles upon these uncharted territories? That's where the significance of OOD detection comes into play. Real-world Implications of Ignoring OOD When neural networks confront out-of-distribution (OOD) data, the results can be less than ideal. A significant performance drop in real-world tasks is one of the immediate consequences. Think of it as a seasoned sailor suddenly finding themselves in uncharted waters, unsure how to navigate.  Moreover, the repercussions can be severe in critical domains. For instance, an AI system with OOD brittleness in medicine might misdiagnose a patient, leading to incorrect treatments. Similarly, in home robotics, a robot might misinterpret an object or a command, resulting in unintended actions. The dangers are real, highlighting the importance of detecting and handling OOD data effectively. The Ideal AI System Deep neural networks, the backbone of many modern AI systems, are typically trained under the closed-world assumption. This assumption presumes that the test data distribution closely mirrors the training data distribution. However, the real world seldom adheres to such neat confines.  When these networks face unfamiliar, out-of-distribution (OOD) data, their performance can wane dramatically. While such a dip might be tolerable in applications like product recommendations, it becomes a grave concern in critical sectors like medicine and home robotics. Even a minor misstep due to OOD brittleness can lead to catastrophic outcomes. An ideal AI system should be more adaptable. It should generalize to OOD examples and possess the acumen to flag instances that stretch beyond its understanding. This proactive approach ensures that when the system encounters data, it can't confidently process, it seeks human intervention rather than making a potentially erroneous decision. For a deeper dive into the intricacies of deep learning and its foundational concepts, check out this comprehensive guide on Demystifying Deep Learning. Understanding OOD Brittleness Deep neural networks, the linchpin of many AI systems, are trained with the closed-world assumption. This assumption presumes that the test data distribution closely resembles the training data distribution. However, the real world often defies such neat confines.  When these networks encounter unfamiliar, out-of-distribution (OOD) data, their performance can deteriorate significantly. While such a decline might be tolerable in applications like product recommendations, it becomes a grave concern in critical sectors like medicine and home robotics. Even a minor misstep due to OOD brittleness can lead to catastrophic outcomes. Why Models Exhibit OOD Brittleness The brittleness of models, especially deep neural networks, to OOD data is multifaceted. Let's delve deeper into the reasons: Model Complexity: Deep neural networks are highly parameterized, allowing them to fit complex patterns in the training data. While this complexity enables them to achieve high accuracy on in-distribution data, it can also make them susceptible to OOD data. The model might respond confidently to OOD inputs, even if they are nonsensical or far from the training distribution. Lack of Regularization: Regularization techniques, like dropout or weight decay, can improve a model's generalization. However, models can still overfit the training data if not applied or tuned correctly, making them brittle to OOD inputs. Dataset Shift: The data distribution can change over time in real-world applications. This phenomenon, known as dataset shift, can lead to situations where the model encounters OOD data even if it was not present during training. Model Assumptions: Many models, especially traditional statistical models, make certain assumptions about the data. If OOD data violate these assumptions, the model's performance can degrade. High Dimensionality: The curse of dimensionality can also play a role. Most of the volume in high-dimensional spaces is near the surface, making it easy for OOD data to lie far from the training data, causing models to extrapolate unpredictably. Adversarial Inputs: OOD data can sometimes be adversarial, crafted explicitly to deceive the model. Such inputs can exploit the model's vulnerabilities, causing it to make incorrect predictions with high confidence. Absence of OOD Training Samples: If a model has never seen examples of OOD data during training, it won't have learned to handle them. This is especially true for supervised learning models, which rely on labeled examples. Model's Objective Function: The objective function optimized during training (e.g., cross-entropy loss for classification tasks) might not penalize confident predictions on OOD data. This can lead to overly confident models even when they shouldn't be. Incorporating techniques to detect and handle OOD data is crucial, especially as AI systems are increasingly deployed in real-world, safety-critical applications. Types of Generalizations Models generalize in various ways, each with its implications for OOD detection. Some models might have a broad generalization, making them more adaptable to diverse data but potentially less accurate.  Others might have a narrow focus, excelling in specific tasks, but could be more comfortable when faced with unfamiliar data. Understanding the type of generalization a model employs is crucial for anticipating its behavior with OOD data and implementing appropriate detection mechanisms. Pre-trained Models vs. Traditional Models Pre-trained models, like BERT, have gained traction in recent years for their impressive performance across a range of tasks. One reason for their robustness against OOD data is their extensive training on diverse datasets. This broad exposure allows them to recognize and handle a wider range of inputs than traditional models that might be trained on more limited datasets.  For instance, a research paper titled "Using Pre-Training Can Improve Model Robustness and Uncertainty" highlighted that while pre-training might not always enhance performance on traditional classification metrics, it significantly bolsters model robustness and uncertainty estimates. This suggests that the extensive and diverse training data used in pre-training these models equips them with a broader understanding, making them more resilient to OOD data. However, even pre-trained models are not immune to OOD brittleness, emphasizing the need for continuous research and refinement in this domain. Approaches to Detect OOD Instances Detecting out-of-distribution (OOD) instances is crucial for ensuring the robustness and reliability of machine learning models, especially deep neural networks. Several approaches have been proposed to address this challenge, each with advantages and nuances. Here, we delve into some of the prominent techniques. Maximum Softmax Probability Softmax probabilities can serve as a straightforward metric for OOD detection. Typically, a neural network model would output higher softmax probabilities for in-distribution data and lower probabilities for OOD data. By setting a threshold on these probabilities, one can flag instances below the threshold as potential OOD instances. Ensembling of Multiple Models Ensembling involves leveraging multiple models to make predictions. For OOD detection, the idea is that while individual models might be uncertain about an OOD instance, their collective decision can be more reliable. By comparing the outputs of different models, one can identify prediction discrepancies, which can indicate OOD data. Temperature Scaling Temperature scaling is a post-processing technique that calibrates the softmax outputs of a model. By adjusting the "temperature" parameter, one can modify the confidence of the model's predictions. Properly calibrated models can provide more accurate uncertainty estimates, aiding OOD detection. Training a Binary Classification Model as a Calibrator Another approach is to train a separate binary classification model that acts as a calibrator. This model is trained to distinguish between the in-distribution and OOD data. By feeding the outputs of the primary model into this calibrator, one can obtain a binary decision on whether the instance is in distribution or OOD. Monte-Carlo Dropout Dropout is a regularization technique commonly used in neural networks. Monte-Carlo Dropout involves performing dropout at inference time and running the model multiple times. The variance in the model's outputs across these runs can provide an estimate of the model's uncertainty, which can be used to detect OOD instances. Research in OOD Detection Deep learning models, particularly neural networks, have performed remarkably in various tasks. However, their vulnerability to out-of-distribution (OOD) data remains a significant concern. Recent research in 2023 has delved deeper into understanding this vulnerability and devising methods to detect OOD instances effectively. Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness (Liu et al., 2020): This paper emphasizes the need for AI systems to detect OOD instances beyond their capability and proposes a method for uncertainty estimation. Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices (Sastry & Oore, 2019): The study presents a method for detecting OOD examples using in-distribution examples and gram matrices, demonstrating its effectiveness in detecting far-from-distribution OOD examples.  Energy-based Out-of-distribution Detection (NeurIPS 2020): Proposing a unified framework for OOD detection, this research uses an energy score to detect anomalies. Learning Confidence for Out-of-Distribution Detection in Neural Networks (13 Feb 2018): The paper highlights that modern neural networks, despite their power, often fail to recognize when their predictions might be incorrect. The research delves into this aspect, aiming to improve confidence in OOD detection. Datasets and Benchmark Numbers In the realm of Out-of-Distribution (OOD) detection, several datasets have emerged as the gold standard for evaluating the performance of various detection methods. Here are some of the most popular datasets and their respective benchmark scores: Benchmark Dataset CIFAR-10 and CIFAR-100 are staple datasets in the computer vision community, often used to benchmark OOD detection methods. For instance, the DHM method has been tested on CIFAR-10 and CIFAR-100 and vice versa, showcasing its robustness. STL-10: Another dataset in the computer vision domain, the Mixup (Gaussian) method, has been applied here, demonstrating its effectiveness in OOD detection. MS-1M vs. IJB-C: This dataset comparison has seen the application of the ResNeXt50 + FSSD method, further emphasizing the importance of robust OOD detection techniques in diverse datasets. Fashion-MNIST: A dataset that's become increasingly popular for OOD detection, with methods like PAE showcasing their prowess. 20 Newsgroups: This dataset, more textual, has seen the application of the 2-Layered GRU method, highlighting the versatility of OOD detection across different data types. It's crucial to note that the benchmark scores of methods can vary based on the dataset, emphasizing the need for comprehensive testing across multiple datasets to ensure the robustness of OOD detection methods. OOD Detector Future Direction The field of OOD detection is rapidly evolving, with new methodologies and techniques emerging regularly. As AI systems become more integrated into real-world applications, the importance of robust OOD detection will only grow. Future research is likely to focus on: Enhanced Generalization: As models become more complex, it will be paramount to ensure they can generalize well to unseen data. This will involve developing techniques that can handle the vast diversity of real-world data. Integration with Other AI Domains: OOD detection will likely see integration with other AI domains, like transfer learning, few-shot learning, and more, to create holistic systems that are both robust and adaptable. Real-time OOD Detection: Real-time OOD detection will be crucial for applications like autonomous driving or medical diagnostics. Research will focus on making OOD detection methods faster without compromising on accuracy. Ethical Considerations: As with all AI advancements, the ethical implications of OOD detection will come to the fore. Ensuring that these systems are fair, transparent, and don't perpetuate biases will be a significant area of focus. With the pace of advancements in the field, the next few years promise to be exciting for OOD detection, with groundbreaking research and applications on the horizon. Out-of-Distribution Detection: Key Takeaways Out-of-distribution (OOD) detection, a pivotal algorithm in the AI landscape, is a cornerstone in modern AI systems.  As AI continues to permeate diverse sectors, from image classification in healthcare to pattern recognition in finance, identifying and handling out-of-distribution samples deviating from the input data the model was trained on becomes paramount.  Here are the pivotal takeaways from our exploration: Significance of OOD Detection: AI models, especially convolutional neural networks, are optimized for their training data. When faced with out-of-distribution data, their activations can misfire, and their performance can drastically plummet, leading to unreliable or even hazardous outcomes in real-world applications. Model Vulnerability: Despite their prowess and intricate loss function designs, models exhibit OOD brittleness primarily due to their training regimen. Their hyper-fine-tuning can make them less adaptable to unfamiliar inputs, emphasizing the need for novelty detection. Diverse Approaches: Researchers are exploring many techniques to enhance OOD detection, from leveraging generative models like variational autoencoders (VAE) to the ensembling of multiple models and from segmentation techniques to validation using Monte-Carlo dropout. Research Landscape: 2023 has seen groundbreaking research in OOD detection, with methods like DHM and PAE leading the charge. Platforms like Arxiv and GitHub have been instrumental in disseminating this knowledge. Datasets like CIFAR-10 serve as baselines for evaluating these novel techniques, and international conferences like ICML have been platforms for such discussions. Future Trajectory: The AI community, with contributions from researchers like Hendricks, Ren, and Chen, is gearing towards enhanced model generalization, real-time OOD detection using self-supervised and unsupervised techniques, and integrating ethical considerations into OOD methodologies. In essence, while being a technical challenge, OOD detection is a necessity in ensuring that AI systems, whether they employ classifier systems or delve into outlier detection, remain reliable, safe, and effective in diverse real-world scenarios.

Sep 15 2023

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Guide to Panoptic Segmentation

The term "panoptic" is derived from two words: "pan," meaning "all," and "optic," signifying "vision."  Panoptic segmentation, a pivotal concept in computer vision, offers a comprehensive approach to image segmentation. It stands out by simultaneously segmenting objects and classifying them. Thus, panoptic segmentation can be interpreted as viewing everything within a given visual field. This technique is a hybrid, merging semantic and instance segmentation strengths.  Introduced by Alexander Kirillov and his team in 2018, panoptic segmentation aims to provide a holistic view of image segmentation rather than relying on separate methodologies. A key distinction of panoptic segmentation is its ability to classify objects into two broad categories: "things" and "stuff." In computer vision, "things" refer to countable objects with a defined geometry, such as cars or animals. On the other hand, "stuff" pertains to objects identified primarily by texture and material, like the sky or roads. Understanding Image Segmentation What is Image Segmentation? Image segmentation, a pivotal concept in computer vision, involves partitioning a digital image into multiple segments, often called image regions or objects. This process transforms an image into a more meaningful and easier-to-analyze representation. Image segmentation assigns labels to pixels so those with the same label share specific characteristics. This technique is instrumental in locating objects and boundaries within images. For instance, in medical imaging, segmentation can create 3D reconstructions from CT scans using geometry reconstruction algorithms. Types of Image Segmentation Semantic Segmentation: This approach identifies the class each pixel belongs to. For instance, in an image with multiple people, all pixels associated with persons will have the same class label, while the background pixels will be classified differently. For a deeper dive into a related topic, check out this comprehensive Guide to Semantic Segmentation on Encord's blog.    Instance Segmentation: Every pixel is identified for its specific belonging instance of the object. It's about detecting distinct objects of interest in the image. For example, in an image with multiple people, each person would be segmented as a unique object. Panoptic Segmentation: A combination of semantic and instance segmentation, panoptic segmentation identifies the class each pixel belongs to while distinguishing between different instances of the same class. What is Panoptic Segmentation? The term "panoptic" derives from encompassing everything visible in a single view. In computer vision, panoptic segmentation offers a unified approach to segmentation, seamlessly merging the capabilities of both instance and semantic segmentation. Panoptic segmentation is not just a mere combination of its counterparts but a sophisticated technique that classifies every pixel in an image based on its class label while identifying the specific instance of that class it belongs to. For instance, in an image with multiple cars, panoptic segmentation would identify each car and distinguish between them, providing a unique instance ID for each. This technique stands out from other segmentation tasks in its comprehensive nature. While semantic segmentation assigns pixels to their respective classes without distinguishing between individual instances, and instance segmentation identifies distinct objects without necessarily classifying every pixel, panoptic segmentation does both. Every pixel in an image processed using panoptic segmentation would have two associated values: a label indicating its class and an instance number. Pixels that belong to "stuff" regions, which are harder to quantify (like the sky or pavement), might have an instance number reflecting that categorization or none at all. In contrast, pixels belonging to "things" (countable objects like cars or people) would have unique instance IDs. This advanced segmentation technique has potential applications in various fields, including medical imaging, autonomous vehicles, and digital image processing. Its ability to provide a detailed understanding of images makes it a valuable tool in the evolving landscape of computer vision. Working Mechanism Panoptic segmentation has emerged as a groundbreaking technique in computer vision. It's a hybrid approach that beautifully marries the strengths of semantic and instance segmentation. While semantic segmentation classifies each pixel into a category, instance segmentation identifies individual object instances. On the other hand, panoptic segmentation does both: it classifies every pixel and assigns a unique instance ID to distinguishable objects. One of the state-of-the-art methods in panoptic segmentation is the Efficient Panoptic Segmentation (EfficientPS) method. This technique leverages deep learning and neural networks to achieve high-quality segmentation results. EfficientPS is designed to be both efficient in terms of computational resources and effective in terms of segmentation quality. It employs feature pyramid networks and convolutional layers to process input images and produce segmentation masks. The method also utilizes the COCO dataset for training and validation, ensuring that the models are exposed to diverse images and scenarios. The beauty of panoptic segmentation, especially methods like EfficientPS, lies in their ability to provide a detailed, pixel-level understanding of images. This is invaluable in real-world applications such as autonomous vehicles, where understanding the category (road, pedestrian, vehicle) and the individual instances (specific cars or people) is crucial for safe navigation. Key Components of Panoptic Segmentation Imagine a painter who not only recognizes every object in a scene but also meticulously colors within the lines, ensuring each detail stands out. That's the magic of panoptic segmentation in the world of computer vision. By understanding its key components, we can grasp how it effectively delineates and classifies every pixel in an image, ensuring both coherence and distinction. Fully Convolutional Network (FCN) and Mask R-CNN Fully Convolutional Networks (FCN) have emerged as a pivotal component in the panoptic segmentation. FCN's strength lies in its ability to process images of varying sizes and produce correspondingly-sized outputs. This network captures patterns from uncountable objects, such as the sky or roads, by classifying each pixel into a semantic label. It's designed to operate end-to-end, from pixel to pixel, offering a detailed, spatially dense prediction. Fully Convolutional Neural Networks Conversely, Mask R-CNN, an extension of the Faster R-CNN, plays a crucial role in recognizing countable objects. While Faster R-CNN is adept at bounding box recognition, Mask R-CNN adds a parallel branch for predicting an object mask. This means that for every detected object, Mask R-CNN identifies it and generates a high-quality segmentation mask for each instance. This dual functionality makes it an invaluable tool for tasks requiring object detection and pixel-level segmentation, such as identifying and distinguishing between individual cars in a traffic scene. Mask RCNN Architecture FCN and Mask R-CNN form the backbone of panoptic segmentation, ensuring that every pixel in an image is accurately classified and, if applicable, associated with a unique instance ID. EfficientPS Architecture One of the foundational elements of this architecture is Efficient Panoptic Segmentation (EfficientPS). EfficientNet is a model designed to systematically scale the network depth, width, and resolution. This ensures the model achieves optimal performance across various tasks without consuming excessive computational resources. A significant aspect of the EfficientPS architecture is the two-way Feature Pyramid Network (FPN). The FPN is adept at handling different scales in an image, making it invaluable for tasks that require understanding both the broader scene and the finer details. This two-way FPN ensures that features from both low-level and high-level layers of the network are utilized, providing a rich set of features for the segmentation task. Fusing outputs from semantic and instance segmentation is another hallmark of the EfficientPS architecture. While semantic segmentation provides a class label for each pixel, instance segmentation identifies individual object instances. EfficientPS combines these outputs, ensuring that every pixel in an image is classified and associated with a unique instance ID if it belongs to a countable object. What makes EfficientPS truly special is its loss function. The architecture employs a compound loss that combines the losses from semantic and instance segmentation tasks. This ensures the model is trained to perform optimally on both tasks simultaneously. The EfficientPS architecture, integrating EfficientNet, two-way FPN, and a compound loss function, set a new benchmark in panoptic segmentation, delivering state-of-the-art results across various datasets.  Prediction using EfficientPS Practical Applications of Panoptic Segmentation Medical Imaging Panoptic segmentation has made significant strides in medical imaging. Panoptic segmentation offers a detailed and comprehensive view of medical images by leveraging the power of both semantic and instance segmentation. This is particularly beneficial in tumor cell detection, where the model identifies the presence of tumor cells and differentiates between individual cells. Such precision is crucial for accurate diagnoses, enabling medical professionals to devise more effective treatment plans. Using datasets like COCO and Cityscapes, combined with deep learning algorithms, ensures that the segmentation models are trained on high-quality data, further enhancing their accuracy in medical diagnoses. Autonomous Vehicles The world of autonomous vehicles is another domain where panoptic segmentation proves its mettle. For self-driving cars, understanding the environment is paramount. Panoptic segmentation aids in this by providing a pixel-level understanding of the surroundings. It plays a pivotal role in distance-to-object estimation, ensuring the vehicle can make informed decisions in real-time. By distinguishing between countable objects (like pedestrians and other vehicles) and uncountable objects (like roads and skies), panoptic segmentation ensures safer navigation for autonomous vehicles. Digital Image Processing Modern smartphone cameras are a marvel of technology, and panoptic segmentation enhances their capabilities. Features like portrait mode, bokeh mode, and auto-focus leverage the power of image segmentation to differentiate between the subject and the background. This allows for the creation of professional-quality photos with depth effects. The fusion of semantic and instance segmentation ensures that the camera can identify and focus on the subject while blurring out the background, resulting in stunning photographs. Research in Panoptic Segmentation With the integration of advanced algorithms and neural networks, the research community has been pushing the boundaries of what's possible in computer vision. One notable model that has emerged as a leader in this space is the "OneFormer (ConvNeXt-L, single-scale, 512x1024),  which has set new benchmarks, especially on the Cityscapes val dataset. Important Papers 2023 has seen the publication of several influential papers that have shaped the trajectory of panoptic segmentation.  Panoptic Feature Pyramid Networks: This paper delves into a minimally extended version of Mask R-CNN with FPN, referred to as Panoptic FPN. The study showcases how this model is a robust and accurate baseline for semantic and instance segmentation tasks. Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation: The work introduces Panoptic-DeepLab, a system designed for panoptic segmentation. The paper emphasizes its simplicity, strength, and speed, aiming to establish a solid baseline for bottom-up methods. OneFormer: This model has emerged as a leader in panoptic segmentation in 2023, setting new benchmarks, especially on the Cityscapes val dataset. Panoptic Segmentation: Key Takeaways Panoptic segmentation has emerged as a pivotal technique in computer vision, offering a comprehensive approach to image segmentation. This method seamlessly integrates the strengths of semantic and instance segmentation, providing a holistic view of images. Let's recap the significant insights and applications of panoptic segmentation: Unified Approach: Panoptic segmentation is a hybrid technique that combines semantic and instance segmentation best. It assigns every pixel in an image a class label while distinguishing between individual object instances. This unified approach ensures every pixel has a clear, singular label, eliminating ambiguities. Diverse Applications: The applications of panoptic segmentation are vast and varied. In the medical field, it aids in precise tumor cell detection, enhancing the accuracy of diagnoses. For autonomous vehicles, it plays a crucial role in distance-to-object estimation, ensuring safer navigation. Additionally, panoptic segmentation enhances smartphone camera capabilities in digital image processing, enabling features like portrait mode and auto-focus. Innovative Research: The field has witnessed rapid advancements, with state-of-the-art models like EfficientPS pushing the boundaries of what's possible. These models leverage architectures like EfficientNet and Feature Pyramid Networks to deliver high-quality segmentation results efficiently. Datasets and Benchmarks: Research in panoptic segmentation is supported by many datasets, with Cityscapes being notable. The benchmark scores on these datasets provide a clear metric to gauge the performance of various models, guiding further research and development. Future Trajectory: The future of panoptic segmentation looks promising. With continuous research and integration of deep learning techniques, we can expect even more accurate and efficient models. These advancements will further expand the applications of panoptic segmentation, from healthcare to autonomous driving and beyond. Panoptic segmentation stands at the intersection of technology and innovation, offering solutions to complex computer vision challenges. As research progresses and technology evolves, its potential applications and impact on various industries will only grow.

Sep 13 2023

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5 Recent AI Research Papers

3D Gaussian Splatting for Real-Time Radiance Field Rendering The paper presents a novel real-time radiance field rendering technique using 3D Gaussian splatting, addressing the challenge of efficient and high-quality rendering. Objective: Develop a real-time radiance field rendering technique Problem: Achieving real-time display rates for rendering unbounded and complete scenes at 1080p resolution using Radiance Field methods. Existing approaches often involve costly neural network training and rendering or sacrifice quality for speed, making it difficult to attain both high visual quality and real-time performance for such scenes.  Solution Anisotropic 3D Gaussians as a high-quality, unstructured representation of radiance fields An optimization technique for 3D Gaussian properties, coupled with adaptive density control, to generate top-tier representations for captured scenes A fast, differentiable GPU-based rendering approach that incorporates visibility awareness, enables anisotropic splatting and supports swift backpropagation to accomplish exceptional quality view synthesis. Methodology  Scene Representation with 3D Gaussians: Begin with sparse points obtained during camera calibration. Utilize 3D Gaussians to represent the scene. Preserve key characteristics of continuous volumetric radiance fields. Avoid unnecessary computations in empty areas of the scene. Optimization and Density Control of 3D Gaussians: Implement interleaved optimization and density control for the 3D Gaussians. Focus on optimizing the anisotropic covariance to achieve precise scene representation. Fine-tune Gaussian properties to enhance accuracy. Fast Visibility-Aware Rendering Algorithm. Develop a rapid rendering algorithm designed for GPUs: Ensure visibility awareness in the rendering process. Enable anisotropic splatting for improved rendering quality. Accelerate training processes. Facilitate real-time rendering for efficient visualization of the radiance field. Find the code implementation on GitHub.   Results 3D Gaussian Splatting for Real-Time Radiance Field Rendering  Achieved real-time rendering of complex radiance fields, allowing for interactive and immersive experiences. Demonstrated significant improvements in rendering quality and performance compared to previous methods like InstantNGP and Plenoxels. Showcased the adaptability of the system through dynamic level-of-detail adjustments, maintaining visual fidelity while optimizing resource usage. Validated the effectiveness of 3D Gaussian splatting in handling radiance field rendering challenges. Read the original paper by Bernhard Kerbl, Georgios, Kopanas, Thomas Lemkühler: 3D Gaussian Splatting for Real-Time Radiance Field Rendering   Nougat: Neural Optical Understanding for Academic Documents Nougat aims to enhance the accessibility of scientific knowledge stored in digital documents, especially PDFs, by proposing Nougat, which performs OCR tasks. It is an academic document PDF parser that understands LaTeX math and tables. Objective: Enhance the accessibility of scientific knowledge stored in digital documents, particularly in PDF format.  Problem: Effectively preserving semantic information, particularly mathematical expressions while converting PDF-based documents into a machine-readable format (LaTex). Solution: Nougat is a vision transformer that enables end-to-end training for the task at hand. This architecture builds upon the Donut architecture and does not require any OCR-related inputs or modules, as the text is recognized implicitly by the network. Nougat: Neural Optical Understanding for Academic Documents Methodology Encoder: Receives document image. Crops margins and resizes the image to a fixed rectangle. Utilizes a Swin Transformer, splitting the image into windows and applying self-attention layers. Outputs a sequence of embedded patches. Decoder: Inputs the encoded image. Uses a transformer decoder architecture with cross-attention. Generates tokens in an auto-regressive manner. Projects the output to match the vocabulary size. Implementation: Adopts mBART decoder from BART. Utilizes a specialized tokenizer for scientific text, similar to Galactica’s approach. Find the code implementation on GitHub.   Results Mathematical expressions had the lowest agreement with the ground truth, mainly due to missed formulas by GROBID and challenges in equation prediction accuracy stemming from bounding box quality. Nougat, both in its small and base versions, consistently outperformed the alternative approach across all metrics, demonstrating its effectiveness in converting document images to compatible markup text. Read the original paper from Meta AI by Lukas Blecher, Guillem Cucurull, Thomas Scialom, and Robert Stojnic: Nougat: Neural Optical Understanding for Academic Documents.   Scaling up GANs for Text-to-Image Synthesis The paper introduces GigaGAN, a highly scalable GAN-based generative model for text-to-image synthesis, achieving exceptional scale, speed, and controllability compared to previous models. Objective: Alternative to auto-regressive and diffusion models for text-to-image synthesis. Problem: Making GANs more scalable and efficient in handling large datasets and generating high-quality, high-resolution images while maintaining stability and enabling fine-grained control over the generative process. Solution: GANs reintroduced as a multi-scale training scheme aim to improve the alignment between images and text descriptions and enhance the generation of low-frequency details in the output images. Methodology  The GigaGAN architecture consists of the following - Generator: Text Encoding branch: utilizes a pre-trained CLIP model to extract text embeddings and a learned attention layer. Style mapping network: produces a style vector similar to StyleGAN Synthesis Network: uses style vector as modulation and text embeddings as attention to create an image pyramid Sample-adaptive kernel selection: chooses convolution kernels based on input text conditioning Discriminator: The image branch of the discriminator makes independent predictions for each scale within the image pyramid. The text branch handles text in a manner similar to the generator, while the image branch operates on an image pyramid, providing predictions at multiple scales. Find the code for evaluation on GitHub.   Results Scale Advancement: GigaGAN is 36 times larger in terms of parameter count than StyleGAN2. It is 6 times larger than StyleGAN-XL and XMC-GAN. Quality Performance: Despite its impressive scale, GigaGAN does not show quality saturation concerning model size. Achieves a zero-shot FID (Fréchet Inception Distance) of 9.09 on the COCO2014 dataset, which is lower than DALL·E 2, Parti-750M, and Stable Diffusion. Efficiency: GigaGAN is orders of magnitude faster at image generation, taking only 0.13 seconds to generate a 512px image. High-Resolution Synthesis: It can synthesize ultra-high-resolution images at 4k resolution in just 3.66 seconds. Latent Vector Control: GigaGAN offers a controllable latent vector space, enabling various well-studied controllable image synthesis applications, including style mixing, prompt interpolation, and prompt mixing. Read the original paper by Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park: Scaling up GANs for Text-to-Image Synthesis.    Code Llama: Open Foundation Models for Code Code Llama is a cutting-edge code-specialized language model, forged through extended training on code-specific datasets, delivering enhanced coding capabilities and support for a range of programming languages. Objective: Build a large language model (LLM) that can use text prompts to generate and discuss code. Problem: A specialized language model for code generation and understanding, with focus on performance, context handling, infiling, instruction following Solution: The proposed solution is Code Llama which is available as three variants: Code Llama: foundational code model Code Llama-Python specialized: for Python Code Llama-Instruct: fine-tuned model for understanding natural language instructions Methodology Code Llama is a specialized model built upon Llama 2. It was developed by extended training on code-specific datasets, including increased data sampling and longer training. Find the code for implementation on GitHub.   Results Code Llama achieves state-of-the-art performance among open models on several code benchmarks: Scores of up to 53% on HumanEval and scores of up to 55% on MBPP. Code Llama - Python 7B outperforms Llama 2 70B on both HumanEval and MBPP benchmarks. All variants of Code Llama models outperform every other publicly available model on the MultiPL-E benchmark. Read the original paper by Meta AI: Code Llama: Open Foundation Models for Code.   FaceChain: A Playground for Identity-Preserving Portrait Generation FaceChain is a personalized portrait generation framework that combines advanced LoRA models and perceptual understanding techniques to create your Digital-Twin. Objective: A personalized portrait generation framework that generates images from a limited set of input images. Problem: The limitations of existing personalized image generation solutions, including the inability to accurately capture key identity characteristics and the presence of defects like warping, blurring, or corruption in the generated images. Solution: FaceChain is a framework designed to preserve the unique characteristics of faces while offering versatile control over stylistic elements in image generation. FaceChain is the integration of two LoRA models into the Stable Diffusion model. This integration endows the model with the capability to simultaneously incorporate personalized style and identity information, addressing a critical challenge in image generation. Methodology Integration of LoRA Models: FaceChain incorporates LoRA models to improve the stability of stylistic elements and maintain consistency in preserving identity during text-to-image generation. Style and Identity Learning: Two LoRA models are used, namely the style-LoRA model and face-LoRA model. The style-LoRA model focuses on learning information related to portrait style, while the face-LoRA model focuses on preserving human identities. Separate Training: These two models are trained separately. The style-LoRA model is trained offline, while the face-LoRA model is trained online using user-uploaded images of the same human identity. Quality Control: To ensure the quality of input images for training the face-LoRA model, FaceChain employs a set of face-related perceptual understanding models. These models normalize the uploaded images, ensuring they meet specific quality standards such as appropriate size, good skin quality, correct orientation, and accurate tags. Weighted Model Integration: During inference, the weights of multiple LoRA models are merged into the Stable Diffusion model to generate personalized portraits. Post-Processing: The generated portraits undergo a series of post-processing steps to further enhance their details and overall quality. Find the code implementation on GitHub.   Results FaceChain: A Playground for Identity-Preserving Portrait Generation Read the original paper by Alibaba Group: FaceChain: A Playground for Identity-Preserving Portrait Generation.  

Sep 12 2023

sampleImage_image-thresholding-image-processing

Image Thresholding in Image Processing

In digital image processing, thresholding is the simplest method of segmenting images. It plays a crucial role in image processing as it allows for the segmentation and extraction of important information from an image. By dividing an image into distinct regions based on pixel intensity or pixel value, thresholding helps distinguish objects or features of interest from the background. This technique is widely used in various applications such as object detection, image segmentation, and character recognition, enabling efficient analysis and interpretation of digital images. Additionally, image thresholding can enhance image quality by reducing noise and improving overall visual clarity.  Thresholding — Image Processing The choice of thresholding technique is critical determination of the accuracy and effectiveness of image analysis. Different thresholding techniques have their own strengths and limitations. Selecting the appropriate technique depends on factors such as image complexity, noise levels, and the desired outcome. Therefore, it is essential to give careful consideration to the selection and to conduct experimentation to ensure optimal results in image processing tasks.  In the article, we will cover the following: What is Image Thresholding?  Image Thresholding Techniques Applications of Image Thresholding  Practical Implementation and Considerations Challenges with Image Thresholding Future Developments in Image Thresholding Image Thresholding: Key Takeaways What is Image Thresholding? Image thresholding involves dividing an image into two or more regions based on intensity levels, allowing for easy analysis and extraction of desired features. By setting a threshold value, pixels with intensities above or below the threshold can be classified accordingly This technique aids in tasks such as object detection, segmentation, and image enhancement.  Image thresholding is a technique that simplifies a grayscale image into a binary image by classifying each pixel value as either black or white based on its intensity level or gray-level compared to the threshold value. This technique reduces the image to only two levels of intensity, making it easier to identify and isolate objects of interest. Binary image conversion allows for efficient processing and analysis of images, enabling various computer vision applications such as edge detection and pattern recognition.  In imaging processing algorithms, the principle of pixel classification based on intensity threshold is widely used. By setting a specific threshold value, pixels with intensity levels above the threshold are classified as white, while those below the threshold are classified as black. This principle forms the foundation for various image enhancement techniques that help to extract important features from an image for further analysis.  In data science and image processing, an entropy-based approach to image thresholding is used to optimize the process of segmenting specific types of image, often those with intricate textures or diverse patterns. By analyzing the entropy, which measures information randomness, this technique seeks to find the optimal threshold value that maximizes the information gained when converting the image into a binary form through thresholding. This approach is especially beneficial for images with complex backgrounds or varying lighting conditions. Through this technique, the binary thresholding process becomes finely tuned, resulting in more accurate segmentation and enhanced feature extraction, which is vital for applications in image analysis and computer vision tasks. Image Thresholding Techniques These are widely used in various fields such as medical imaging, computer vision, and remote sensing. These techniques are essential for accurate image processing and interpretation. They help to convert grayscale or color images into binary images, separating the foreground from the background, allowing for better segmentation and extraction of features from an image, which is crucial for various applications in computer vision and pattern recognition. Global Thresholding Global Thresholding is a widely used technique where a single threshold value is applied to an entire image. However, this technique  may not be suitable for images with varying lighting conditions or complex backgrounds. To overcome this limitation, adaptive thresholding techniques may be employed, which adjust the threshold value locally based on the characteristics of each pixel's neighborhood. These techniques are particularly useful in scenarios where there is significant variation in illumination across different regions of the image.  Thresholding-Based Image Segmentation Simple thresholding is a basic technique that assigns a binary value to each pixel based on a global threshold value. It is effective when the image has consistent lighting conditions and a clear foreground-background separation. However, when images contain varying lighting conditions or complex backgrounds, adaptive thresholding techniques are more suitable. These techniques dynamically adjust the threshold value for each pixel based on its local neighborhood, allowing for better segmentation and accurate object detection.  Otsu's Method for Automatic Threshold Determination is a widely used technique for automatically determining the optimal threshold value in image segmentation. It calculates the threshold by maximizing the between-class variance of pixel value, which effectively separates foreground and background regions. This method is particularly useful when dealing with images that have bimodal or multimodal intensity distributions, as it can accurately identify the threshold that best separates different objects or regions in the image.  Otsu's method - Wikipedia “A nonparametric and unsupervised method of automatic threshold selection for picture segmentation. An optimal threshold is selected by the discriminant criterion, so as to maximize the separability of the resultant classes in gray levels. The procedure utilizies only the zeroth- and the first-order cumulative moments of the gray-level histogram.” - Nobuyuki Otsu   Pros and Cons of Global Thresholding  Gobal thresholding offers several advantages, including its simplicity and efficiency in determining a single threshold value for the entire image. It is particularly effective in scenarios where the foreground and background regions have distinct intensity distributions. However, global thresholding may not be suitable for images with complex intensity distributions or when there is significant variation in lighting conditions across the image. Additionally, it may not accurately segment objects or regions that have overlapping intensity values.  Local (Adaptive) Thresholding  Local thresholding addresses the limitations of global thresholding by considering smaller regions within the image. It calculates a threshold value for each region based on its local characteristics, such as mean or median intensity. This approach allows for better adaptability to varying lighting conditions and complex intensity distributions, resulting in more accurate segmentation of objects or regions with overlapping intensity values. However, local thresholding may require more computational resources and can be sensitive to noise or uneven illumination within the image, which can affect the overall performance of the segmentation algorithm. Adaptive Thresholds for Different Image Regions are needed to overcome the challenges of variations in lighting conditions and contrast within an image. These adaptive thresholds help improve the accuracy and clarity of object or region detection. This approach involves dividing the image into smaller sub-regions and calculating a threshold value for each sub-region based on its local characteristics. By doing so, the algorithm can better account for these variations and mitigate the effects of noise or uneven illumination, as each sub-region is treated independently.  The simplest method to segment an image is thresholding. Using the thresholding method, segmentation of an image is done by fixing all pixels whose intensity values are more than the threshold to a foreground value.   Mean and Gaussian Adaptive Thresholding  Two commonly used methods in image processing are Mean and Gaussian Adaptive Thresholding. Mean adaptive thresholding calculates the threshold value for each sub-region by taking the average intensity of all pixels within that region. On the other hand, Gaussian adaptive thresholding uses a weighted average of pixel intensities, giving more importance to pixels closer to the center of the sub-region. These methods are effective in enhancing image quality and improving accuracy in tasks such as object detection or segmentation.   Advantages over Global Thresholding  Adaptive Thresholding has advantages over global thresholding. One advantage is that it can handle images with varying lighting conditions or uneven illumination. This is because adaptive thresholding calculates the threshold value locally, taking into account the specific characteristics of each sub-region. Additionally, adaptive thresholding can help preserve important details and fine textures in an image, as it adjusts the threshold value based on the local pixel intensities.  Applications of Image Thresholding  Image thresholding is a technique used in computer vision that has a variety of applications, including image segmentation, object detection, and character recognition. By separating objects from their background in an image, image thresholding makes it easier to analyze and extract relevant information. Optical character recognition (OCR) systems, for example, use image thresholding to distinguish between foreground (text) and background pixels in scanned documents, making them editable. Additionally, QR codes, which encode information within a grid of black and white squares, can be incorporated into images as a form of data representation and retrieval. Real-world applications  Object Detection: By setting a threshold value, objects can be separated from the background, allowing for more accurate and efficient object detection.  Medical Images: Image thresholding can be used to segment different structures or abnormalities for diagnosis and analysis in medical imaging. Quality Control: Image thresholding plays a crucial role in quality control processes, such as inspecting manufactured products for defects or ensuring consistency in color and texture of a color image. Object Segmentation: Image thresholding is also commonly used in computer vision tasks such as object segmentation, where it helps to separate foreground objects from the background. This enables more accurate and efficient detection of objects within an image. Noise Reduction: Thresholding can be utilized for noise reduction, as it can help to eliminate unwanted artifacts or disturbances in an image.   Edge Detection: Image thresholding aids in identifying and highlighting the boundaries between different objects or regions within an image with edge detection algorithms.  A step by step guide to Image Segmentation in Computer Vision can be read here. Thresholding Practical Implementation and Considerations  When implementing thresholding techniques, it is important to carefully select the appropriate threshold value based on the specific image and desired outcome. This can be achieved through experimentation or through the use of adaptive thresholding methods that automatically adjust the threshold based on local image characteristics. Furthermore, it is essential to consider the potential trade-off between noise reduction and preserving important details in the image, as aggressive thresholding may lead to the loss of valuable information.  Steps for implementing thresholding algorithms (Python) Here are step-by-step guides for implementing image thresholding algorithms using Python. You will implement the global thresholding and Otsu's thresholding, which are two commonly used thresholding techniques. Implementing Image Thresholding Algorithms in Python Global Thresholding Let us review what we know so far, and for this you can use Google Colab to run the below code. #Install the required library !pip install opencv–python #Get the image file !wget -O /content/sunflower.jpg 'https://images.unsplash.com/photo-1503936380431-4a4ce0fc296c?ixlib=rb4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=2970&q=80' After acquiring the image file, right-click on it to copy the image path, and proceed to paste it into the designated section labeled "ADD YOUR FILE PATH HERE." If you are using an alternative IDE, you can alternatively input the path on your local system. import cv2 from google.colab.patches import cv2_imshow # Read the image#image = cv2.imread('ADD YOUR FILE PATH HERE', cv2.IMREAD_GRAYSCALE) image = cv2.imread('/content/sunflower.jpg', cv2.IMREAD_GRAYSCALE) # Apply global thresholding _, binary_image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY) # Display the results cv2_imshow(image) cv2_imshow(binary_image) cv2.waitKey(0) cv2.destroyAllWindows() Output: Grayscale Image Binary Image Otsu's Thresholding import cv2 from google.colab.patches import cv2_imshow # Read the image image = cv2.imread('/content/sunflower.jpg', cv2.IMREAD_GRAYSCALE) # Define the desired width and height for the resized image desired_width = 640 # Change this to your desired width desired_height = 480 # Change this to your desired height # Resize the image to the desired size resized_image = cv2.resize(image, (desired_width, desired_height)) # Apply Otsu's thresholding _, binary_image = cv2.threshold(resized_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # Display the results cv2_imshow(resized_image) cv2_imshow(binary_image) cv2.waitKey(0) cv2.destroyAllWindows() Output: Otsu’s Thresholding Image The code above applies Otsu's thresholding to the image and displays the original image and binary image or thresholded image. Remember to replace `'image.jpg'` with the actual path of your image file. These examples demonstrate the basic implementation of global thresholding and Otsu's thresholding in both Python. You can further customize these codes to suit your specific image processing needs, including pre-processing steps, visualization enhancements, and additional algorithm parameters. Global Thresholding Value in Python using Otsu’s Method import cv2 import numpy as np from google.colab.patches import cv2_imshow # Read the image in grayscale image = cv2.imread('/content/sunflower.jpg', cv2.IMREAD_GRAYSCALE) # Define the desired width and height for the resized image desired_width = 640 # Change this to your desired width desired_height = 480 # Change this to your desired height # Resize the image to the desired size resized_image = cv2.resize(image, (desired_width, desired_height)) # Calculate global threshold using Otsu's method _, global_thresholded = cv2.threshold(resized_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # Calculate Otsu's threshold value directly otsu_threshold_value = cv2.threshold(resized_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[0] # Display the results cv2_imshow(global_thresholded) print("Global Threshold Value:", otsu_threshold_value) cv2.waitKey(0) cv2.destroyAllWindows() Output: Global Threshold Value: 168.0 This code will display the original image and the image after global thresholding using Otsu's method, along with the threshold value determined by Otsu's algorithm. Pre-processing and post-processing impact  Pre-processing and post-processing techniques play a crucial role in achieving accurate and meaningful results in image thresholding. Employing a range of techniques before and after thresholding can significantly enhance the accuracy of segmentation and the usability of the final binary image. Pre-processing techniques such as noise reduction, image enhancement, and morphological operations before thresholding, can improve segmentation results. Similarly, post-processing techniques like connected component analysis and contour smoothing can further refine the binary image and remove any artifacts or imperfections.  Let's delve deeper into how pre-processing and post-processing impact image thresholding Pre-processing Impact Noise reduction techniques like Gaussian smoothing or median filtering techniques help suppress noise while preserving important edges and details. Contrast Enhancement of an image before thresholding can lead to better separation between object and background intensities. Histogram equalization or adaptive histogram equalization techniques lead to better separation between object and background intensities. Basically the image histogram will be greatly affected if the image is thresholded as shown in the figure below. Histogram transformations Illumination Correction is nothing but background subtraction or morphological operations normalize illumination across the image, especially in cases where lighting conditions are non-uniform or uneven. Edge detection techniques can be applied as a pre-processing step to identify significant edges in the image. This can assist in defining regions of interest and guide the thresholding process, especially when the boundaries between objects and background are not well-defined. Image Smoothing can be done using smoothing filters like Gaussian blur or mean filtering can reduce fine details and minor variations in the image, simplifying the thresholding process and leading to more coherent segmentation results. Post-processing Impact Connected Component Analysis identifies and labels separate regions in the binary image, distinguishing individual objects and eliminating isolated noise pixels. Morphological Operations like erosion and dilation fine-tune the binary image by removing small noise regions and filling in gaps between segmented objects. Object Size Filtering removes small objects or regions that are unlikely to be relevant, especially when dealing with noise or artifacts that may have been segmented as objects during thresholding. Smoothing Edges is achieved when smoothing filters applied to the binary image, and can result in cleaner and more natural-looking object boundaries. Object Feature Extraction involves area, perimeter, centroid, and orientation, and can be used for further analysis or classification. Object Merging and Splitting techniques can be applied to merge nearby objects or split overly large ones in cases where thresholding results in objects that are too fragmented or split. Pre-processing and post-processing steps are integral to obtaining accurate and meaningful results in image thresholding. The selection of appropriate techniques and their parameters should be guided by the specific characteristics of the image and the goals of the analysis. By thoughtfully combining pre-processing and post-processing techniques, it is possible to transform raw images into segmented binary images that provide valuable insights for various applications. Challenges with Image Thresholding  There are several challenges with image thresholding. Some of the main challenges are determining an appropriate threshold value, handling noise and variations in lighting conditions, and dealing with complex image backgrounds. Furthermore, selecting the right pre-processing and post-processing techniques can be difficult,  as it requires a deep understanding of the image content and the desired outcome. Overcoming these challenges requires careful consideration and experimentation..  The challenge of thresholding continuous antibody measures Some of the challenges of image thresholding include high computational cost, insufficient performance, lack of generalization and flexibility, lack of capacity to capture various image degradations, and many more. Image thresholding presents distinct challenges when dealing with complex images and varying lighting conditions. These challenges can impact the accuracy of segmentation results and require careful consideration to achieve reliable outcomes. Let's delve into the specific challenges posed by complex images and varying lighting conditions: Complex Images Complex Intensity Distributions: Images with complex intensity distributions, such as multi-modal or non-uniform distributions, can make selecting an appropriate threshold value difficult. Traditional thresholding methods that assume a bi-modal distribution might struggle to accurately segment objects when intensity values are spread across multiple peaks. Gradual Intensity Transitions: Objects with gradual intensity changes or subtle edges can be challenging to segment accurately. Traditional thresholding methods are designed to work best with well-defined edges, and they might lead to fragmented or imprecise segmentation when applied to images with gradual transitions. Overlapping Objects: Objects that overlap or occlude each other in the image can cause difficulties for thresholding. In such cases, a single threshold might segment a merged object as multiple objects, or vice versa. This can lead to inaccurate object separation and hinder subsequent analysis. Texture and Pattern Variability: Images with intricate textures or complex patterns can be tough to segment accurately. Traditional thresholding, which relies on intensity values alone, might not effectively capture the variations in textures, leading to under-segmentation or over-segmentation. Partial Occlusion: When an object is only partially visible due to occlusion or truncation, thresholding methods can struggle to define the boundaries accurately. Incomplete segmentation can lead to errors in size, shape, and feature measurements. Multiple Object Types: Images containing multiple types of objects with varying shapes, sizes, and intensities pose a challenge for uniform thresholding. Adapting the threshold value to cater to these diverse objects can be complex. Varying Lighting Conditions Uneven Illumination: Images captured under uneven or non-uniform lighting conditions can result in inaccurate segmentation using global thresholding. Objects illuminated differently might not be accurately separated from the background, leading to segmentation errors. Shadows and Highlights: Varying lighting conditions can create shadows and highlights, altering the perceived intensity values of objects. Shadows can cause objects to be under-segmented, while highlights can lead to over-segmentation. Local Intensity Variations: In the presence of varying lighting, the assumption of consistent intensity values across an object might not hold true. Adaptive thresholding methods that consider local intensity characteristics are better suited to handle such scenarios. Dynamic Scenes: Images captured in dynamic environments with changing lighting conditions, such as outdoor scenes or real-time video feeds, require continuous adjustment of threshold values to account for the evolving illumination. Static thresholding might result in poor segmentation. Reflections and Glare: Reflective surfaces or glare can cause spikes in intensity values, complicating the thresholding process. These spikes can be misleading and result in the misclassification of pixels. Addressing these challenges requires a combination of techniques, including adaptive thresholding methods, pre-processing steps, and post-processing refinements. Adaptive thresholding takes into account local intensity variations and is particularly effective in dealing with varying lighting conditions. Pre-processing steps, such as contrast enhancement and illumination normalization, can help mitigate the effects of uneven lighting. Post-processing techniques, like morphological operations and edge smoothing, can refine the segmentation results and eliminate artifacts. Image Thresholding in varying Lighting Conditions Furthermore, the integration of machine learning techniques, like convolutional neural networks (CNNs), can enhance segmentation accuracy for complex images and varying lighting conditions. These approaches learn from data and can adapt to the intricacies of the image content. Overall, understanding the unique challenges presented by complex images and varying lighting conditions and applying appropriate techniques is crucial for successful image thresholding in these scenarios.  Future Developments in Image Thresholding  Upcoming advancements in image processing include the integration of deep learning algorithms, which can further enhance segmentation accuracy by automatically learning and extracting features from complex images. Furthermore, advancements in hardware technology, such as the development of specialized processors for image processing tasks, may also contribute to faster and more efficient image thresholding in the future.  The potential impact of emerging technologies in image thresholding is signficant. With the integration of deep learning algorithms, we can expect more accurate and precise segmentation results, leading to improved applications in fields like medical imaging, autonomous vehicles, and object recognition. Furthermore, advancements in hardware technology can significantly enhance the speed and efficiency of image thresholding algorithms, enabling real-time processing and analysis of large-scale image datasets.  Image Thresholding: Key Takeaways Crucial Role of Thresholding: Image thresholding is vital for segmenting images, extracting features, and enhancing image quality. It's used in object detection, segmentation, and character recognition, aiding efficient image analysis. Technique Selection Importance: Choosing the right thresholding technique is crucial. Different methods have strengths and limitations, based on image complexity, noise, and goals. Careful consideration is essential for optimal results. Binary Conversion: Image thresholding simplifies images by converting them to binary form (black and white). This simplification aids in isolating objects and features of interest. Global and Adaptive Thresholding: Global thresholding is straightforward but not suitable for complex backgrounds. Adaptive thresholding adjusts locally, making it effective for varying lighting conditions. Otsu's Method and Applications: Otsu's method automatically determines optimal thresholds, especially useful for complex images. Thresholding finds applications in object detection, segmentation, edge detection, and quality control. Implementation and Challenges: Implementing thresholding involves selecting thresholds, pre-processing, and post-processing. Challenges include noise, lighting variations, complex backgrounds, and overlapping objects.

sampleImage_barlow-twins-self-supervised-learning

Barlow Twins: Self-Supervised Learning

Self-supervised learning (SSL) has emerged as a transformative paradigm in machine learning, particularly in computer vision applications. Unlike traditional supervised learning, where labeled data is a prerequisite, SSL leverages unlabeled data, making it a valuable approach when labeled datasets are scarce. The essence of SSL lies in its ability to process data of lower quality without compromising the ultimate outcomes. This approach mirrors how humans learn to classify objects more closely, extracting patterns and correlations from the data autonomously. However, a significant challenge in SSL is the potential for trivial, constant solutions. A naive SSL method trivially classifies every example as positive in binary classification, leading to a constant and uninformative solution.  This challenge underscores the importance of designing robust algorithms, such as the Barlow Twins, that can effectively leverage the power of SSL while avoiding pitfalls like trivial solutions. In the subsequent sections, we will delve deeper into the Barlow Twins approach to SSL, a new approach developed by Yann LeCun and the team at Facebook. We will also explore its unique features, benefits, and contribution to the ever-evolving landscape of self-supervised learning in machine learning. The Barlow Twins Approach The Barlow Twins method, named in homage to neuroscientist H. Barlow's redundancy-reduction principle, presents a novel approach to self-supervised learning (SSL). This method is particularly significant in computer vision, where SSL has rapidly bridged the performance gap with supervised methods. Central to the Barlow Twins approach is its unique objective function, designed to naturally prevent the collapse often observed in other SSL methods. This collapse typically results in trivial, constant solutions, a challenge many SSL algorithms grapple with. The Barlow Twins method addresses this by measuring the cross-correlation matrix between the outputs of two identical neural networks. These networks are fed with distorted versions of a sample, and the objective is to make this matrix as close to the identity matrix as possible. The role of the cross-correlation matrix is pivotal. By ensuring that the embedding vectors of distorted versions of a sample are similar, the method minimizes redundancy between the components of these vectors. This enhances the quality of the embeddings and ensures that the learned representations are robust and invariant to the applied distortions. Image Classification with Barlow Twins Barlow Twin Architecture Imagine you have many images of cats and dogs, but they must be labeled. You want to train a machine-learning model to distinguish between cats and dogs using this unlabeled dataset. Here is the Barlow Twins approach: Data Augmentation: Create two distorted versions for each image in the dataset. For instance, one version might be a cropped section of the original image, and the other might be the same cropped section but with altered brightness or color. Twin Neural Networks: Use two identical neural networks (the "twins"). Feed one distorted version of the image into the first network and the other distorted version into the second network. Objective Function: The goal is to make the outputs (embeddings) of the two networks as similar as possible for the same input image, ensuring that the networks recognize the two distorted versions as being of the same class (either cat or dog). At the same time, the Barlow Twins method aims to reduce redundancy in the embeddings. This is achieved by ensuring that the cross-correlation matrix of the embeddings from the two networks is close to an identity matrix. In simpler terms, the method ensures that each embedding component is as independent as possible from the other components. Training: The twin networks are trained using the above objective. Over time, the networks learn to produce similar embeddings for distorted versions of the same image and different embeddings for images of different classes (cats vs. dogs). Representation Learning: Once trained, you can use one of the twin networks (or both) to extract meaningful representations (embeddings) from new images. These representations can then be used with a simple linear classifier for various tasks, such as classification. Barlow Twins Loss Function The primary objective of the Barlow Twins method is to reduce redundancy in the representations learned by neural networks. To achieve this, the method uses two identical neural networks (often called "twins") that process two distorted versions of the same input sample. The goal is to make the outputs (or embeddings) of these networks as similar as possible for the same input while ensuring that the individual components of these embeddings are not redundant. The Barlow Twins loss function is designed to achieve this objective. It is formulated based on the cross-correlation matrix of the outputs from the two networks. Cross-Correlation Matrix Calculation: Let's say the outputs (embeddings) from the two networks for a batch of samples are Y1 and Y2. The cross-correlation matrix C is computed as the matrix product of the centered outputs of the two networks, normalized by the batch size. Loss Function: The diagonal elements of the matrix C represent the correlation of each component with itself. The method aims to make these diagonal elements equal to 1, ensuring that the embeddings from the two networks are similar. The off-diagonal elements represent the correlation between different components. The method aims to make these off-diagonal elements equal to 0, ensuring that the components of the embeddings are not redundant. The loss is then computed as the sum of the squared differences between the diagonal elements and the squared values of the off-diagonal elements. Pseudocode for Barlow Twins The Barlow Twins approach can be applied to more complex datasets and tasks beyond simple image classification. The key idea is to leverage the structure in unlabeled data by ensuring that the learned representations are consistent across distortions and non-redundant. Redundancy Reduction Principle Horace Basil Barlow, a renowned British vision scientist, significantly contributed to our understanding of the visual system. One of his most influential concepts was the redundancy reduction principle. Barlow posited that one of the primary computational aims of the visual system is to reduce redundancy, leading to the efficient coding hypothesis4. In simpler terms, while adjacent points in images often have similar brightness levels, the retina minimizes this redundancy, ensuring that the information processed is as concise and non-redundant as possible. The Barlow Twins method in self-supervised learning draws inspiration from this principle. By reducing redundancy, the Barlow Twins approach aims to create embeddings invariant to distortions and statistically independent across different parts of an image. This ensures that the neural networks, when trained with this method, produce representations that capture the essential features of the data while discarding superfluous information. In machine learning and computer vision, applying Barlow's redundancy reduction principle through the Barlow Twins method offers a promising avenue for achieving state-of-the-art results in various tasks, from image classification to segmentation. Key Features of Barlow Twins Independence from Large Batches One of the standout features of the Barlow Twins method is its independence from large batches. In deep learning, especially with extensive datasets, large batch sizes are often employed to expedite training. However, this can lead to challenges, including the need for significant GPU memory and potential generalization issues. The Barlow Twins approach, in contrast, does not necessitate large batches. This independence is particularly advantageous for those without access to extensive computational resources. The method's design, which emphasizes measuring the cross-correlation matrix between the outputs of two identical networks fed with distorted versions of a sample, ensures that the embeddings produced are invariant to these distortions. By aiming to make this matrix as close to the identity matrix as possible, the Barlow Twins method effectively minimizes redundancy between the components of the embedding vectors, irrespective of the batch size. Another noteworthy aspect is the method's resilience to overfitting. Since it doesn't rely on large batches, the risk of the model memorizing the training data, a common pitfall in machine learning, is substantially reduced. This ensures the trained models are more robust and can generalize to unseen data. The Barlow Twins approach's design, emphasizing redundancy reduction and independence from large batches, sets it apart in self-supervised learning methods. Its unique features make it resource-efficient and ensure its applicability and effectiveness across various tasks and computational settings. Symmetry in Network Twins The Barlow Twins approach is distinctive in its utilization of two identical neural networks, often called "twins". This symmetry departs from many other self-supervised learning methods that rely on predictor networks, gradient stopping, or moving averages to achieve their objectives. The beauty of this symmetric design lies in its simplicity and efficiency. By feeding distorted versions of a sample into these twin networks and then comparing their outputs, the Barlow Twins method ensures that the produced embeddings are invariant to the distortions. This symmetry eliminates the need for additional complexities like predictor networks, often used to map representations from one network to another. The absence of gradient stopping and moving averages in the Barlow Twins approach means that the training process is more straightforward and less prone to potential pitfalls associated with these techniques. Gradient stopping, for instance, can sometimes hinder the optimization process, leading to suboptimal results. In essence, the symmetric design of the Barlow Twins method not only simplifies the training process but also enhances the robustness and effectiveness of the learned representations. By focusing on redundancy reduction and leveraging the power of symmetric network twins, the Barlow Twins approach offers a fresh perspective in the ever-evolving landscape of self-supervised learning. Benefits of High-Dimensional Output Vectors The Barlow Twins approach has garnered attention for its unique take on self-supervised learning, particularly in its use of high-dimensional output vectors. But why does this matter? High-dimensional vectors allow for a richer data representation in neural networks. The Barlow Twins method can capture intricate patterns and nuances in the data that might be missed with lower-dimensional representations when using very high-dimensional vectors. This depth of representation is crucial for tasks like image recognition in computer vision, where subtle differences can be the key to accurate classification. Moreover, the Barlow Twins method leverages these high-dimensional vectors to ensure that the embeddings produced by the twin networks are both similar (due to the distorted versions of a sample) and minimally redundant. This balance between similarity and non-redundancy is achieved through the redundancy reduction principle, inspired by neuroscientist H. Barlow. To illustrate, imagine describing a complex painting using only a few colors. While you might capture the general theme, many details must be recovered. Now, imagine having a vast palette of colors at your disposal. The richness and depth of your description would be incomparably better. Similarly, high-dimensional vectors offer a richer "palette" for neural networks to represent data. Using very high-dimensional vectors in the Barlow Twins method allows for a more detailed and nuanced understanding of data, paving the way for more accurate and robust machine learning models. Performance and Comparisons The Barlow Twins approach has been a significant leap forward in self-supervised learning, particularly when benchmarked against the ImageNet dataset.13 ImageNet is a large-scale dataset pivotal for computer vision tasks and is a rigorous testing ground for novel algorithms and methodologies. In semi-supervised classification, especially in scenarios where data is limited, the Barlow Twins method has showcased commendable performance. The method outperforms previous methods on ImageNet for semi-supervised classification in the low-data regime. This is particularly noteworthy as working with limited data often challenges training robust models. With its unique approach to redundancy reduction and high-dimensional output vectors, the Barlow Twins method captures intricate patterns in the data, leading to improved classification results. Moreover, using a linear classifier head, the Barlow Twins approach aligns with the current state-of-the-art ImageNet classification. It also holds its ground in transfer tasks of classification and object detection.13 These results underscore the potential of the Barlow Twins method in pushing the boundaries of self-supervised learning, especially in computer vision tasks. ImageNet numbers for Barlow Twin SSL approach The Barlow Twins approach to SSL focuses on learning embeddings that remain invariant to input sample distortions. A significant challenge in this domain has been the emergence of trivial, constant solutions. While most contemporary methods have circumvented this issue through meticulous implementation nuances, the Barlow Twins approach introduces an objective function that inherently prevents such collapses.6 The Barlow Twins algorithm exhibits certain features when combined with other SSL methods. For instance, SimCLR and BYOL, two state-of-the-art SSL baselines, rely heavily on negative samples and data augmentations, respectively. In contrast, the Barlow Twins method sidesteps the need for negative samples, focusing instead on minimizing the redundancy between embeddings. This approach, combined with large batches and a tailored learning rate, has been instrumental in its success. Furthermore, the Barlow Twins algorithm has been tested on the ImageNet dataset, a large-scale computer vision benchmark. The results were compelling. Using a ResNet-50 encoder and a projector network, the Barlow Twins achieved a 67.9% top-1 accuracy after 100 epochs. This performance is particularly noteworthy when considering the algorithm's simplicity and the projector network's absence of batch normalization or ReLU. It's worth noting that the Barlow Twins' performance and comparisons are actively discussed on various platforms, including GitHub, where developers and researchers share their insights and modifications to the algorithm. As the field of SSL continues to grow, it will be intriguing to see how the Barlow Twins evolve and where they stand across different SSL methods. Barlow Twins: Key Takeaways The Importance of Redundancy Reduction The Barlow Twins method has been recognized for its innovative application of the Redundancy Reduction principle in self-supervised learning (SSL).6 This principle, inspired by neuroscientist H. Barlow, emphasizes the significance of reducing redundant information while retaining essential features. In the context of the Barlow Twins, this means creating embeddings invariant to distortions of the input sample while avoiding trivial constant solutions. The method achieves this by measuring the cross-correlation matrix between the outputs of two identical networks fed with distorted versions of a sample and ensuring it remains close to the identity matrix. This intricate balance ensures that the embeddings of distorted versions of a sample are alike, yet the redundancy between the components of these vectors is minimized. Advantages Over Other SSL Methods The Barlow Twins approach offers several unique advantages over other SSL methods. One of its standout features is its ability to naturally avoid the collapse of embeddings without needing large batches or asymmetry between the twin networks. This is achieved without using techniques like gradient stopping, predictor networks, or moving averages on weight updates. Furthermore, the method benefits from high-dimensional output vectors, allowing for richer data representation and improved performance in tasks like image recognition. The future looks promising as SSL narrows the gap with supervised methods, especially in large computer vision benchmarks. With its unique approach and advantages, the Barlow Twins method is poised to play a pivotal role in developing SSL methods. The potential for further research lies in refining the method, exploring its application in diverse domains, and integrating it with other advanced techniques to push the boundaries in SSL.

Sep 11 2023

sampleImage_vision-transformers

Introduction to Vision Transformers (ViT)

In the rapidly evolving landscape of artificial intelligence, a paradigm shift is underway in the field of computer vision.  Vision Transformers, or ViTs, are transformative models that bridge the worlds of image analysis and self-attention-based architectures. These models have shown remarkable promise in various computer vision tasks, inspired by the success of Transformers in natural language processing. In this article, we will explore Vision Transformers, how they work, and their diverse real-world applications. Whether you are a seasoned AI enthusiast or just beginning in this exciting field, join us on this journey to understand the future of computer vision. What is a Vision Transformer? The Vision Transformers, or ViTs for short, combine two influential fields in artificial intelligence: computer vision and natural language processing (NLP).  The Transformer model, originally proposed in the paper titled "Attention Is All You Need" by Vaswani et al. in 2017, serves as the foundation for ViTs. Transformers were designed as a neural network architecture that excels in handling sequential data, making them ideal for NLP tasks. ViTs bring the innovative architecture of Transformers to the world of computer vision.  The state-of-the-art large language models GPT by OpenAI and BERT by Google leverage transformers to model contextual information in text. BERT focuses on bidirectional representations and GPT on autoregressive generation. Vision Transformers vs Convolutional Neural Networks In computer vision, Convolutional Neural Networks (CNNs) have traditionally been the preferred models for processing and understanding visual data. However, a significant shift has occurred in recent years with the emergence of Vision Transformers (ViTs). These models, inspired by the success of Transformers in natural language processing, have shown remarkable potential in various computer vision tasks.  CNN Dominance For decades, Convolutional Neural Networks (CNNs) have been the dominant models used in computer vision. Inspired by the human visual system, these networks excel at processing visual data by leveraging convolutional operations and pooling layers. CNNs have achieved impressive resultsin various image-related tasks, earning their status as the go-to models for image classification, object detection, and image segmentation. Application of Convolutional Neural Network Method in Brain Computer Interface A convolutional network comprises layers of learnable filters that convolve over the input image. These filters are designed to detect specific features, such as edges, textures, or more complex patterns. Additionally, pooling layers downsample the feature maps, gradually reducing the spatial dimensions while retaining essential information. This hierarchical approach allows CNNs to learn and represent hierarchical features, capturing intricate details as they progress through the network. Read Convolutional Neural Networks (CNN) Overview for more information.   Vision Transformer Revolution While CNNs have been instrumental in computer vision, a paradigm shift has emerged with the introduction of Vision Transformers (ViTs). ViTs leverage the innovative Transformer architecture, originally designed for sequential data, and apply it to image understanding. CNNs operate directly on pixel-level data, exploiting spatial hierarchies and local patterns. In contrast, ViTs treat images as sequences of patches, borrowing a page from NLP where words are treated as tokens. This fundamental difference in data processing coupled with the power of self-attention, enables ViTs to learn intricate patterns and relationships within images, gives ViTs a unique advantage. The Vision Transformer (ViT) model was proposed in An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Alexey Dosovitskiy et al. This represented a significant breakthrough in the field, as it is the first time a Transformer encoder has been trained on ImageNet with superior performance to conventional convolutional architectures. How do Vision Transformers Work? Transformer Foundation To gain an understanding of how Vision Transformers operate, it is essential to understand the foundational concepts of the Transformer architecture like self-attention. Self-attention is a mechanism that allows the model to weigh the importance of different elements in a sequence when making predictions, leading to impressive results in various sequence-based tasks. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Adapting the Transformer for Images The concept of self-attention has been adapted for processing images with the use of Vision Transformers. Unlike text data, images are inherently two-dimensional, comprising pixels arranged in rows and columns. To address this challenge, ViTs convert images into sequences that can be processed by the Transformer. Split an image into patches: The first step in processing an image with a Vision Transformer is to divide it into smaller, fixed-size patches. Each patch represents a local region of the image. Flatten the patches: Within each patch, the pixel values are flattened into a single vector. This flattening process allows the model to treat image patches as sequential data. Produce lower-dimensional linear embeddings: These flattened patch vectors are then projected into a lower-dimensional space using trainable linear transformations. This step reduces the dimensionality of the data while preserving important features. Add positional encodings: To retain information about the spatial arrangement of the patches, positional encodings are added. These encodings help the model understand the relative positions of different patches in the image. Feed the sequence into a Transformer encoder: The input to a standard Transformer encoder comprises the sequence of patch embeddings and positional embeddings. This encoder is composed of multiple layers, each containing two critical components: multi-head self-attention mechanisms (MSPs), responsible for calculating attention weights to prioritize input sequence elements during predictions, and multi-layer perceptron (MLP) blocks. Before each block, layer normalization (LN) is applied to appropriately scale and center the data within the layer, ensuring stability and efficiency during training. During the training, an optimizer is also used to adjust the model's hyperparameters in response to the loss computed during each training iteration. Classification Token: To enable image classification, a special "classification token" is prepended to the sequence of patch embeddings. This token's state at the output of the Transformer encoder serves as the representation of the entire image. Inductive Bias and ViT It's important to note that Vision Transformers exhibit less image-specific inductive bias compared to CNNs. In CNNs, concepts such as locality, two-dimensional neighborhood structure, and translation equivariance are embedded into each layer throughout the model. However, ViTs rely on self-attention layers for global context and only use a two-dimensional neighborhood structure in the initial stages for patch extraction. This means that ViTs rely more on learning spatial relations from scratch, offering a different perspective on image understanding. Hybrid Architecture In addition to the use of raw image patches, ViTs also provide the option for a hybrid architecture. With this approach, input sequences can be generated from feature maps extracted by a CNN. This level of flexibility allows practitioners to combine the strengths of CNNs and Transformers in a single model, offering further possibilities for optimizing performance. The code for the paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" and related projects is accessible on GitHub. This architecture is implemented in PyTorch, with TensorFlow implementations also provided. Real-World Applications of Vision Transformers Now that we have a solid understanding of what Vision Transformers are and how they work, let's explore their machine learning applications. These models have proven to be highly adaptable, thereby potentially transforming various computer vision tasks. Image Classification A primary application of Vision Transformers is image classification, where ViTs serve as powerful classifiers. They excel in categorizing images into predefined classes by learning intricate patterns and relationships within the image, driven by their self-attention mechanisms. Object Detection Object detection is another domain where Vision Transformers are making a significant impact. Detecting objects within an image involves not only classifying them but also precisely localizing their positions. ViTs, with their ability to preserve spatial information, are well-suited for this task. These algorithms can identify objects and provide their coordinates, contributing to advancements in areas like autonomous driving and surveillance. Read Object Detection: Models, Use Cases, Examples for more information. Image Segmentation Image segmentation, which involves dividing an image into meaningful segments or regions, benefits greatly from the capabilities of ViTs. These models can discern fine-grained details within an image and accurately delineate object boundaries. This is particularly valuable in medical imaging, where precise segmentation can aid in diagnosing diseases and conditions. Action Recognition Vision Transformers are also making strides in action recognition, where the goal is to understand and classify human actions in videos. Their ability to capture temporal dependencies, coupled with their strong image processing capabilities, positions ViTs as contenders in this field. They can recognize complex actions in video sequences, impacting areas such as video surveillance and human-computer interaction. Multi-Modal Tasks ViTs are not limited to images alone. They are also applied in multi-modal tasks that involve combining visual and textual information. These models excel in tasks like visual grounding, where they link textual descriptions to corresponding image regions, as well as visual question answering and visual reasoning, where they interpret and respond to questions based on visual content. Transfer Learning One of the remarkable features of Vision Transformers is their ability to leverage pre-trained models for transfer learning. By pre-training on large datasets, ViT models learn rich visual representations that can be fine-tuned for specific tasks with relatively small datasets. This transfer learning capability significantly reduces the need for extensive labeled data, making ViTs practical for a wide range of applications. Vision Transformers: Key Takeaways Vision Transformers (ViTs) represent a transformative shift in computer vision, leveraging the power of self-attention from natural language processing to image understanding. Unlike traditional Convolutional Neural Networks (CNNs), ViTs process images by splitting them into patches, flattening those patches, and then applying a Transformer architecture to learn complex patterns and relationships. ViTs rely on self-attention mechanisms, enabling them to capture long-range dependencies and global context within images, a feature not typically found in CNNs. Vision Transformers have applications in various real-world tasks, including image classification tasks, object detection, image segmentation, action recognition, generative modeling, and multi-modal tasks.

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What is Retrieval Augmented Generation (RAG)?

The large-scale adoption of Artificial Intelligence continues to have a transformative effect on the world. Foundation models, especially Large Language Models (LLMs) like OpenAI's GPT, have gained widespread attention and captivated the general public's imagination. Trained on a vast corpus of online data and possessing the ability to understand and output natural language, LLMs are challenging the very nature of intelligence and creativity.  Yet the precise mechanisms that make state-of-the-art LLMs so effective are also the source of their biggest flaw - their tendency to provide inaccurate, as well as out of date information. LLMs are prone to making things up, and as generative models, they don’t cite sources in their responses. “Language models are not search engines or databases. Hallucinations are unavoidable. What is annoying is that the models generate text with mistakes that is [sic] hard to spot.” - Adrian Tam, A Gentle Introduction to Hallucinations in Large Language Models   What is Retrieval Augmented Generation (RAG)?  Enter Retrieval Augmented Generation, known as RAG,  a framework promising to optimize generative AI and ensure its responses are up-to-date, relevant to the prompt, and most importantly, true. How does RAG work?  The main idea behind RAG is surprisingly simple; combining LLMs with a separate store of content outside of the language model containing sourced and up-to-date information for the LLM to consult before generating a response for its users. In other words, this approach merges information retrieval with text generation. To truly appreciate how this works, it's essential to delve into the realm of deep learning and understand how language models process our prompts and produce responses in natural language. LLMs generate responses based purely on the user’s input and skillful prompt engineering is vital for maximizing the accuracy of the generated responses. This input is turned into embeddings, which are numerical representations of concepts that allow the AI to compute the semantics of what the user is asking.  In the RAG framework, the language model identifies relevant information in an external dataset after computing the embeddings of a user’s query. The LLM then performs a similarity search on the prompt and the external dataset, before fine-tuning the user’s prompt using the relevant information it retrieved. Only then is the prompt sent to the LLM to generate an output for the user. Classic LLM (left) vs one using the RAG framework (right) What makes this framework so effective is that ‘external dataset’ can mean any number of things. For example, these could be APIs, databases that are updated in real-time, or even open domains such as Wikipedia or GitHub. Benefits of Retrieval Augmented Generation (RAG) Combining the user’s prompt with a separate store of information before generating an output has multiple benefits, not least that it allows the LLM to provide sources for the responses it provides.  ‘Classic’ LLMs can only obtain new information during retraining, which is a very expensive and time-consuming process. However, the RAG framework overcomes this challenge by enabling real-time updates and new sources to be incorporated into the external dataset without having to re-train the entire model. This provides LLMs with valuable and specialized knowledge in addition to what’s included in their initial training data. Studies have demonstrated that RAG models surpass non-RAG models across various metrics, including reduced susceptibility to hallucinations and increased accuracy in responses. They are also less likely to leak sensitive personal information. Applications for Retrieval Augmented Generation (RAG) RAG has a wide range of applications across all domains that require specialized on-demand knowledge. Its applications includes, but are not limited to:  Chatbots and AI assistants: RAG models can be leveraged to build advanced question-answering systems superior to classic retrieval based chatbots. They can retrieve relevant information from a knowledge base and generate detailed, context-aware answers to user queries. The AI assistant found in our documentation is a perfect example of this. Education tools: RAG can be employed to develop educational tools that provide students with answers to questions, explanations, and additional context based on textbooks and reference materials. Legal Research and document review: Legal professionals can use RAG models to quickly search and summarize legal documents, statutes, and case law to aid in legal research and document review. Medical diagnosis and healthcare: In the healthcare domain, RAG models can help doctors and other medical professionals access the latest medical literature and clinical guidelines to assist in diagnosis and treatment recommendations. Language translation (with context): By considering the context from a knowledge base, RAG can assist in language translation tasks, resulting in more accurate translations that account for specific terminology or domain knowledge. Retrieval Augmented Generation (RAG): Summary RAG principles have been shown to reduce the frequency and severity of issues related to LLMs in a host of different metrics. The external knowledge sources that LLMs are given access to can vary and easily be kept up-to-date, providing the language models with sources as well as much-needed context for specific tasks and use cases. These embeddings are subsequently combined with the user's input to generate accurate responses.  Maintaining objectivity and accuracy in an online space rife with misinformation is extremely challenging, and since hallucinations are baked into the very fabric of how generative models work it currently seems impossible to imagine a generative AI model that is 100% accurate. However, RAG reminds us that improvements in AI depend as much on well-designed frameworks as they do on advancements in technology. This serves as a reminder as we work on advancing the next generation of deep learning technologies.

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Guide to Transfer Learning

Transfer learning has become an essential technique in the artificial intelligence (AI) domain due to the emergence of deep learning and the availability of large-scale datasets.  This comprehensive guide will discuss the fundamentals of transfer learning, explore its various types, and provide step-by-step instructions for implementing it. We’ll also address the challenges and practical applications of transfer learning. What is Transfer Learning? In machine learning, a model's knowledge resides in its trained weights and biases. These weights are generated after extensive training over a comprehensive training dataset and help understand data patterns for the targeted problem.  Transfer learning is a type of fine-tuning in which the weights of a pre-trained model for an upstream AI task are applied to another AI model to achieve optimal performance on a similar downstream task using a smaller task-specificdataset. In other words, it leverages knowledge gained from solving one task to improve the performance of a related but different task. Since the model already has some knowledge related to the new task, it can learn well from a smaller dataset using fewer training epochs. Intuitive Examples Of Transfer Learning Transfer learning has applications in numerous deep learning projects, such as computer vision tasks like object detection or natural language processing tasks like sentiment analysis. For example, an image classification model trained to recognize cats can be fine-tuned to classify dogs. Since both animals have similar features, the weights from the cat classifier can be fine-tuned to create a high-performing dog classifier. Pre-trained Models Rather than starting a new task from scratch, pre-trained models capture patterns and representations from the training data, providing a foundation that can be leveraged for various tasks. Usually, these models are deep neural networks trained on large datasets, such as the ImageNet dataset for image-related tasks or TriviaQA for natural language processing tasks. Through training, the model acquires a thorough understanding of features, feature representations, hierarchies, and relationships within the data. The Spectrum of Pre-training Methods Several popular pre-trained architectures have epitomized the essence of transfer learning across domains. These include: VGG (Visual Geometry Group), a convolutional neural network architecture widely recognized for its straightforward design and remarkable effectiveness in image classification. Its architecture is defined by stacking layers with small filters, consistently preserving the spatial dimensions of the input. VGG is a starting point for more advanced models like VGG16 and VGG19. ResNet (Residual Network), a convolutional neural network architecture that addresses the vanishing gradient problem using skip connections, enabling the training of very deep networks. It excels in image classification and object detection tasks. BERT (Bidirectional Encoder Representations from Transformers), a pre-trained NLP model that has the ability to understand the context from both directions in a text sequence. Its proficiency in contextual understanding is used in various language-related tasks, such as text classification, sentiment analysis, and more. InceptionV3, a deep learning model based on the CNN architecture. It is widely used for image classification and computer vision tasks. It is a variant of the original GoogLeNet architecture known for its "inception" modules that allow it to capture information at multiple scales and levels of abstraction. Using prior knowledge of images during pre-training, InceptionV3's features can be adapted to perform well on narrower, more specialized tasks. Transferable Knowledge In transfer learning, transferable knowledge serves as the foundation that enables a model's expertise in one area to enhance its performance in another. Throughout the training process, a model accumulates insights that are either domain-specific or generic.  Domain-specific knowledge are relevant to a particular field, like medical imaging. Conversely, generic knowledge tackles more universal patterns that apply across domains, such as recognizing shapes or sentiments. Transferable knowledge can be categorized into two types: low-level features and high-level semantics. Low-level features encompass basic patterns like edges or textures, which are useful across many tasks. High-level semantics, on the other hand, delve into the meaning behind patterns and relationships, making them valuable for tasks requiring context-understanding. Task Similarity & Domains Understanding task similarity is critical to choosing an effective transfer learning approach – fine-tuning or feature extraction – and whether to transfer knowledge within the same domain or bridge gaps across diverse domains. Fine-tuning vs. Feature Extraction: When reusing pre-trained models, there are two main strategies to enhance model performance: fine-tuning and feature extraction. Fine-tuning involves adjusting the pre-trained model's parameters and activations while retraining its learned features. For specific fine-tuning tasks, a dense layer is added to the pre-trained layers to customize the model's outputs and minimize the loss on the new task, aligning them with the specific outcomes needed for the target task. On the other hand, feature extraction involves extracting the embeddings from the final layer or multiple layers of a pre-trained model. The extracted features are fed into a new model designed for the specific task to achieve better results. Usually, feature extraction does not modify the original network structure. It simply computes features from the training data that are leveraged for downstream tasks. Same-domain vs. Cross-domain Transfer: Transfer learning can work within the same domain or across different domains. In same-domain transfer, the source and target tasks are closely related, like recognizing different car models within the automotive domain. Cross-domain transfer involves applying knowledge from a source domain to an unrelated target domain, such as using image recognition expertise from art to enhance medical image analysis. Types of Transfer Learning  Transfer learning can be categorized into different types based on the context in which knowledge is transferred. These types offer insights into how models reuse their learned features to excel in new situations. Categorizations of Transfer Learning Let’s discuss two common types of transfer learning. Inductive Transfer Learning Inductive transfer learning is a technique used when  labeled data is consistent across the source and target domains, but the tasks undertaken by the models are distinct. It involves transferring knowledge across tasks or domains. When transferring across tasks, a model's understanding from one task aids in solving a different yet related task. For instance, using a model trained on image classification improves object detection performance. Transferring across domains extends this concept to different datasets. For instance, a model initially trained on photos of animals can be fine-tuned for medical image analysis. Transductive Transfer Learning In transductive learning, the model has encountered training and testing data beforehand.  Learning from the familiar training dataset, transductive learning makes predictions on the testing dataset. While the labels for the testing dataset might be unknown, the model uses its learned patterns to navigate the prediction process. Transductive transfer learning is applied to scenarios where the domains of the source and target tasks share a strong resemblance but are not precisely the same. Consider a model trained to classify different types of flowers from labeled images (source domain). The target task is identifying flowers in artistic paintings without labels (target domain). Here, the model's learned flower recognition abilities from labeled images are used to predict the types of flowers depicted in the paintings. How to Implement Transfer Learning Transfer learning is a nuanced process that requires deliberate planning, strategic choices, and meticulous adjustments. By piecing together the appropriate strategy and components, practitioners can effectively harness the power of transfer learning. Given a pre-trained model, here are detailed steps for transfer learning implementation. Learning Process of Transfer Learning Dataset Preparation In transfer learning, dataset preparation includes data collection and preprocessing for the target domain. Practitioners acquire labeled data for the target domain. Even though the tasks may differ, the fine-tuning training data should have similar characteristics to the source domain. During data preprocessing, employing techniques like data augmentation can significantly enhance the model's performance. If you want to learn more about data preprocessing, read our detailed blog on Mastering Data Cleaning & Data Preprocessing.   Model Selection & Architecture The process of model selection and architecture design sets the foundation for successful transfer learning. It involves choosing a suitable pre-trained model and intricately adjusting it to align with the downstream task. Deep learning models like VGG, ResNet, and BERT offer a solid foundation to build upon. Freeze the top layers of the chosen pre-trained model to build a base model for the downstream task that captures the general features of the source domain. Then, add layers to the base model to learn task-specific features. Transfer Strategy Transfer learning requires finding the right path to adapt a model's knowledge. Here are three distinct strategies to consider, tailored to different scenarios and data availability. Full Fine-tuning: This approach uses the target data to conduct fine-tuning across the entire model. It's effective when a considerable amount of labeled training data is available for the target task. Layer-wise Fine-tuning: It involves fine-tuning specific layers to adapt the pre-trained model's expertise. This strategy is appropriate when target data is limited. Feature Extraction: It involves holding the pre-trained layers constant and extracting their learned features. New model is trained based on the learned features for the downstream task. This method works well when the target dataset is small. The new model capitalizes on the pre-trained layers' general knowledge. Hyperparameter Tuning Hyperparameter tuning fine-tunes model's performance. These adjustable settings are pivotal in how the model learns and generalizes from data. Here are the key hyperparameters to focus on during transfer learning: Learning Rate: Tune the learning rate for the fine-tuning stage to determine how quickly the model updates its weights by learning from the downstream training data. Batch Size: Adjust the batch size to balance fast convergence and memory efficiency. Experiment to find the sweet spot. Regularization Techniques: Apply regularization methods like dropout or weight decay to prevent overfitting and improve model generalization. If you want to learn more about fine-tuning, read our detailed guide on Fine-tuning Models: Hyperparameter Optimization.   Training & Evaluation Train and compile the downstream model and modify the output layer according to the chosen transfer strategy on the target data. Keep a watchful eye on loss and accuracy as the model learns. Select evaluation metrics that align with the downstream task's objectives. For instance, model accuracy is the usual go-to metric for classification tasks, while the F1 score is preferred for imbalanced datasets. Ensure the model's capabilities are validated on a validation set, providing a fair assessment of its readiness for real-world challenges. Practical Applications of Transfer Learning Transfer learning offers practical applications in many industries, fueling innovation across AI tasks. Let's delve into some real-world applications where transfer learning has made a tangible difference: Autonomous Vehicles The autonomous vehicles industry benefits immensely from transfer learning. Models trained to recognize objects, pedestrians, and road signs from vast datasets can be fine-tuned to suit specific driving environments. For instance, a model originally developed for urban settings can be adapted to navigate rural roads with minimal data. Waymo, a prominent player in autonomous vehicles, uses transfer learning to enhance its vehicle's perception capabilities across various conditions. Healthcare Diagnostics AI applications in the healthcare domain use transfer learning to streamline medical processes and enhance patient care. One notable use is interpreting medical images such as X-rays, MRIs, and CT scans. Pre-trained models can be fine-tuned to detect anomalies or specific conditions, expediting diagnoses swiftly. By leveraging knowledge from existing patient data, models can forecast disease progression and tailor treatment plans. This proves especially valuable in personalized medicine. Moreover, transfer learning aids in extracting insights from vast medical texts, helping researchers stay updated with the latest findings and enabling faster discoveries. The importance of transfer learning is evident in a recent study regarding its use in COVID-19 detection from chest X-ray images. The experiment proposed using a pre-trained network (ResNet50) to identify COVID-19 cases. By repurposing the network's expertise, the model provided swift COVID diagnosis with 96% performance accuracy, demonstrating how transfer learning algorithms accelerate medical advancements. Gaming In game development, pre-trained models can be repurposed to generate characters, landscapes, or animations. Reinforcement learning models can use transfer learning capabilities to initialize agents with pre-trained policies, accelerating the learning process. For example, OpenAI's Dota 2 bot, OpenAI Five, blends reinforcement and transfer learning to master complex real-time gaming scenarios. System Overview of Dota 2 with Large-Scale Deep Reinforcement Learning E-commerce In e-commerce, recommendations based on user behavior and preferences can be optimized using transfer learning from similar user interactions. Models trained on extensive purchasing patterns can be fine-tuned to adapt to specific user segments. Moreover, NLP techniques like Word2Vec's pre-trained word embeddings enable e-commerce platforms to transfer knowledge from large text corpora effectively. This enhances their understanding of customer feedback and enables them to tailor strategies that enhance the shopping experience. Amazon, for instance, tailors product recommendations to individual customers through the transfer learning technique. Cross-lingual Translations The availability of extensive training data predominantly biased toward the English language creates a disparity in translation capabilities across languages. Transfer learning bridges this gap and enables effective cross-lingual translations. Large-scale pre-trained language models can be fine-tuned to other languages with limited training data. Transfer learning mitigates the need for vast language-specific datasets by transferring language characteristics from English language datasets. For example, Google's Multilingual Neural Machine Translation system, Google Translate, leverages transfer learning to provide cross-lingual translations. This system employs a shared encoder for multiple languages, utilizing pre-trained models on extensive English language datasets. Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Limitations of Transfer Learning  While transfer learning enables knowledge sharing, it's essential to acknowledge its limitations. These challenges offer deeper insights to data scientists about areas that demand further attention and innovation. Here are several areas where transfer learning shows limitations: Dataset Bias & Mismatch Transfer learning's effectiveness hinges on the similarity between the source and target domains. If the source data doesn't adequately represent the target domain, models might struggle to adapt accurately. This dataset mismatch can lead to degraded performance, as the model inherits biases or assumptions from the source domain that do not apply to the target domain. If you want to learn more about reducing bias in machine learning, read our detailed blog on How To Mitigate Bias in Machine Learning Models. Overfitting & Generalization Despite its prowess, transfer learning is not immune to overfitting. When transferring knowledge from a vastly different domain, models might over-adapt to the nuances of the source data, resulting in poor generalization to the target task. Striking the right balance using learned features and not overemphasizing source domain characteristics is a persistent challenge. Catastrophic Forgetting Models mastering a new task may inadvertently lose proficiency in the original task. This phenomenon, known as catastrophic forgetting, occurs when sequential retraining for a new task overrides previously acquired knowledge. The new data changes the knowledge-heavy, pre-trained weights of the model, causing the model to lose prior knowledge. Balancing the preservation of existing expertise while acquiring new skills is crucial, particularly in continual learning scenarios. Ethical & Privacy Concerns The emergence of transfer learning has raised ethical questions regarding the origin and fairness of the source data. Fine-tuned models inheriting biases or sensitive information from source domains might perpetuate inequalities or breach privacy boundaries. Ensuring models are ethically trained and the transfer process adheres to privacy norms is an ongoing challenge. Advanced Topics in Transfer Learning As transfer learning advances, it ventures into uncharted territories with various advanced techniques that redefine its capabilities. These innovative methods revolutionize the process of transferring knowledge across domains, enriching model performance and adaptability. Here's a glimpse into some of the advanced topics in transfer learning: Domain Adaptation Techniques Domain adaptation is a critical aspect of transfer learning that addresses the challenge of applying models trained on one domain to perform well in another related domain. Here are two domain adaptation techniques: Self-training: Self-training iteratively labels unlabeled target domain data using the model's predictions. For example, training a sentiment analysis model using labeled data for positive and negative sentiment but unlabeled data for neutral sentiment. The model starts by making predictions on the neutral data and then uses them as "pseudo-labels" to fine-tune itself on the neutral sentiment, gradually improving its performance in this class. Basic Iterative Self-training Pipeline Adversarial Training: Adversarial training pits two models against each other – one adapts to the target domain, while the other attempts to distinguish between source and target data. This sharpens the model's skills in adapting to new domains. Adversarial training also plays a crucial role in strengthening models against adversarial attacks. Exposing the model to these adversarial inputs during training teaches them to recognize and resist such attacks in real-world scenarios. Zero-shot & Few-shot Learning Zero-shot learning involves training a model to recognize classes it has never seen during training, making predictions with no direct examples of those classes. Conversely, few-shot learning empowers a model to generalize from a few examples per class, allowing it to learn and make accurate predictions with minimal training data. Other learning strategies include one-shot learning and meta-learning. With one example per class, one-shot learning replicates the human ability to learn from a single instance. For example, training a model to identify rare plant species using just one image of each species. On the other hand, meta-learning involves training the model on a range of tasks, facilitating its swift transition to novel tasks with minimal data. Consider a model trained on various tasks, such as classifying animals, objects, and text sentiments. When given a new task, like identifying different types of trees, the model adapts swiftly due to its exposure to diverse tasks during meta-training. Multi-modal Transfer Learning Multi-modal transfer learning involves training models to process and understand information from different modalities, such as text, images, audio, and more. These techniques elevate models to become versatile communicators across different sensory domains.  Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models Two prominent types of multi-modal transfer learning are: Image-Text Transfer: This type of transfer learning uses text and visual information to generate outcomes. It is most appropriate for image captioning tasks. Audio-Visual Transfer: Audio-visual transfer learning enables tasks like recognizing objects through sound. This multi-sensory approach enriches the model's understanding and proficiency in decoding complex audio information. Future Trends in Transfer Learning The transfer learning landscape is transformative, with trends set to redefine how models adapt and specialize across various domains. These new directions offer a glimpse into the exciting future of knowledge transfer. Continual Learning & Lifelong Adaptation The future of transfer learning lies in models that continuously evolve to tackle new challenges. Continual learning involves training models on tasks over time, allowing them to retain knowledge and adapt to new tasks without forgetting what they've learned before. This lifelong adaptation reflects how humans learn and specialize over their lifetimes. As models become more sophisticated, the ability to learn from a constant stream of tasks promises to make them even more intelligent and versatile. Federated Transfer Learning Federated Transfer Learning Imagine a decentralized network of models collaborating to enhance each other's knowledge. Federated transfer learning envisions models distributed across different devices and locations, collectively learning from their local data while sharing global knowledge.  This approach respects privacy, as sensitive data remains local while still benefiting from the network's collective intelligence. Federated learning's synergy with transfer learning can democratize AI by enabling models to improve without centralizing data. Improved Pre-training Strategies Pre-training, a key element of transfer learning, is expected to become even more effective and efficient. Models will likely become adept at learning from fewer examples and faster convergence. Innovations in unsupervised pre-training can unlock latent patterns in data, leading to better transfer performance.  Techniques like self-supervised learning, where models learn from the data without human-labeled annotations, can further refine pre-training strategies, enabling models to grasp complex features from raw data. Ethical & Fair Transfer Learning The ethical dimension of transfer learning gains importance as models become more integral to decision-making. Future trends will focus on developing fair and unbiased transfer learning methods, ensuring that models don't perpetuate biases in the source data. Techniques that enable models to adapt while preserving fairness and avoiding discrimination will be crucial in building AI systems that are ethical, transparent, and accountable. Transfer Learning: Key Takeaways  Transfer learning is a dynamic ML technique that leverages pre-trained models to develop new models, saving time and resources while boosting performance. Transfer learning has proven its versatility, from its role in accelerating model training, enhancing performance, and reducing data requirements to its practical applications across industries like healthcare, gaming, and language translation. In transfer learning, it is vital to carefully select pre-trained models, understand the nuances of different transfer strategies, and navigate the limitations and ethical considerations of this approach. Techniques like domain adaptation, zero-shot learning, meta-learning, and multi-modal transfer learning offer more depth in the transfer learning domain. The future of transfer learning promises advanced federated techniques, continual learning, fair adaptation, and improved pre-training strategies.

Sep 05 2023

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Inter-rater Reliability: Definition, Examples, Calculation

Inter-rater reliability measures the agreement between two or more raters or observers when assessing subjects. This metric ensures that the data collected is consistent and reliable, regardless of who is collects or analyzes it. The significance of inter-rater reliability cannot be overstated, especially when the consistency between observers, raters, or coders is paramount to the validity of the study or assessment. Inter-rater reliability refers to the extent to which different raters or observers give consistent estimates of the same phenomenon. It is a measure of consistency or agreement between two or more raters. On the other hand, intra-rater reliability measures the consistency of ratings given by a single rater over different instances or over time. In research, inter-rater reliability is pivotal in ensuring the validity and reliability of study results. In qualitative research, where subjective judgments are often required, having a high degree of inter-rater reliability ensures that the findings are not merely the result of one individual's perspective or bias. Instead, it confirms that multiple experts view the data or results similarly, adding credibility to the findings.1 Moreover, in studies where multiple observers are involved, inter-rater reliability helps standardize the observations, ensuring that the study's outcomes are not skewed due to the variability in observations. Methods to Measure Inter-rater Reliability Inter-rater reliability, often called IRR, is a crucial statistical measure in research, especially when multiple raters or observers are involved. It assesses the degree of agreement among raters, ensuring consistency and reliability in the data collected. Various statistical methods have been developed to measure it, each with unique advantages and applications.1 Cohen's Kappa Cohen's Kappa is a widely recognized statistical method used to measure the agreement between two raters. It considers the possibility of the agreement occurring by chance, providing a more accurate measure than a simple percentage agreement. The Kappa statistic ranges from -1 to 1, where 1 indicates perfect agreement, 0 suggests no better agreement than chance, and -1 indicates complete disagreement.2 The formula for calculating Cohen's Kappa is: Where:  \( p_o \) is the observed proportion of agreement  \( p_e \) is the expected proportion of agreement Using Cohen's Kappa is essential when the data is categorical, and raters may agree by chance. It provides a more nuanced understanding of the reliability of raters. Intraclass Correlation Coefficient (ICC) The Intraclass Correlation Coefficient, commonly known as ICC, is another method used to measure the reliability of measurements made by different raters. It's beneficial when the measurements are continuous rather than categorical. ICC values range between 0 and 1, with values closer to 1 indicating higher reliability. One of the main differences between ICC and Cohen's Kappa is their application. While Cohen's Kappa is best suited for categorical data, ICC is ideal for continuous data. Additionally, ICC can be used for more than two raters, making it versatile in various research settings. Percentage Agreement Percentage agreement is the simplest method to measure inter-rater reliability. It calculates the proportion of times the raters agree without considering the possibility of chance agreement. While it's straightforward to compute, it doesn't provide as nuanced a picture as methods like Cohen's Kappa or ICC. For instance, if two raters agree 85% of the time, the percentage agreement is 85%. However, this method doesn't account for agreements that might have occurred by chance, making it less robust than other methods. Despite its simplicity, it is essential to be cautious when using percentage agreement, especially when the stakes are high, as it might provide an inflated sense of reliability. Factors Affecting Inter-rater Reliability Inter-rater reliability (IRR) is a crucial metric in research methodologies, especially when data collection involves multiple raters. It quantifies the degree of agreement among raters, ensuring that the data set remains consistent across different individuals. However, achieving a high IRR, such as a perfect agreement, is difficult. Several factors can influence the consistency between raters, and comprehending these can aid in enhancing the reliability measures of the data. Rater Training One of the most important factors affecting IRR is the training of raters. Proper training can significantly reduce variability and increase the coefficient of inter-rater agreement. For instance, in Krippendorff's study (2011) study, raters trained using a specific methodology exhibited a Cohen’s Kappa value of 0.85, indicating a high level of agreement, compared to untrained raters with a kappa value of just 0.5.4 Training ensures that all raters understand the rating scale and the criteria they are evaluating against. For example, in clinical diagnoses, raters can be trained using mock sessions where they are presented with sample patient data. Feedback sessions after these mock ratings can pinpoint areas of disagreement, offering a chance to elucidate and refine the methodology. Training and clear guidelines are not just best practices; they're essential. They bridge the gap between subjective judgments and objective evaluations, ensuring research remains unbiased and true to its purpose. Clarity of Definitions The clarity of definitions in the rating process is pivotal. Providing raters with unambiguous definitions, such as elucidating the difference between intra-rater and inter-rater reliability or explaining terms like "percent agreement" versus "chance agreement," ensures consistency. For example, in a research method involving the assessment of academic papers, if "originality" isn't clearly defined, raters might have divergent interpretations. A clear definition of terms in a study involving Krippendorff’s alpha as a reliability measure increased the alpha value from 0.6 to 0.9, indicating a higher degree of agreement.5 Defining the time frame between tests can lead to more consistent results in test-retest reliability assessments. Subjectivity in Ratings Subjectivity, especially in ordinal data, can significantly impede achieving a high IRR. For instance, in a data collection process involving movie reviews, two raters might have different thresholds for what constitutes a "good" film, leading to varied ratings. A Pearson correlation study found that when raters were given a clear guideline, the coefficient increased by 20%.6  To curtail subjectivity, it's imperative to have explicit guidelines. Tools like Excel for data analysis can help visualize areas of high variability. Moreover, employing reliability estimates like Fleiss Kappa or Cronbach's alpha can provide a clearer picture of the degree of agreement. For instance, a Fleiss Kappa value closer to 1 indicates high inter-rater reliability. While tools like the kappa statistic, intra-class correlation coefficient, and observed agreement offer quantifiable metrics, the foundation of high IRR lies in rigorous training, precise definitions, and minimizing subjectivity. Practical Applications and Examples of Inter-rater Reliability Inter-rater reliability (IRR) is used in various research methods to ensure that multiple raters or observers maintain consistency in their assessments. This measure often quantified using metrics such as Cohen’s Kappa or the intra-class correlation coefficient, is paramount when subjective judgments are involved. Let's explore the tangible applications of inter-rater reliability across diverse domains. Clinical Settings In clinical research, IRR is indispensable. Consider a scenario where a large-scale clinical trial is underway. Multiple clinicians collect data, assessing patient responses to a new drug. Here, the level of agreement among raters becomes critical. The trial's integrity is compromised if one clinician records a side effect while another overlooks it. In such settings, metrics like Fleiss Kappa or Pearson's correlation can quantify the degree of agreement among raters, ensuring that the data set remains consistent.7 Furthermore, in diagnoses, the stakes are even higher. A study revealed that when two radiologists interpreted the same X-rays without a standardized rating scale, their diagnoses had a variability of 15%. However, clear guidelines and training reduced the variability to just 3%, showcasing the power of high inter-rater reliability in clinical settings. Social Sciences Social sciences, with their inherent subjectivity, lean heavily on IRR. Multiple researchers conducted observational studies in a study exploring workplace dynamics in English corporate culture. Using tools like Excel for data analysis, the researchers found that the observed agreement among raters was a mere 60% without established guidelines. However, post-training and with clear definitions, the agreement soared to 90%, as measured by Krippendorff’s alpha.9 Education Education, a sector shaping future generations, cannot afford inconsistencies. Consider grading, a process fraught with subjectivity. In a study involving multiple teachers grading the same set of papers, the initial score variability was 20%. However, after a rigorous training session and with a standardized rating scale, the variability plummeted to just 5%.10 Standardized tests are the gateways to numerous opportunities, especially relying on IRR. A disparity in grading can alter a student's future. For instance, a test-retest reliability study found that scores varied by as much as 15 points on a 100-point scale without ensuring inter-rater agreement. Such inconsistencies can differentiate between a student getting their dream opportunity or missing out.10 Inter-rater reliability, quantified using metrics like the kappa statistic, Cronbach's alpha, or the intra-rater reliability measure, is non-negotiable across domains. Whether it's clinical trials, anthropological studies, or educational assessments, ensuring consistency among raters is not just a statistical necessity; it's an ethical one. Inter-rater Reliability: Key Takeaways Inter-rater reliability (IRR) is a cornerstone in various research domains, ensuring that evaluations, whether from clinical diagnoses, academic assessments, or qualitative studies, are consistent across different raters. Its significance cannot be overstated, as it safeguards the integrity of research findings and ensures that subjective judgments don't skew results. IRR is a litmus test for data reliability, especially when multiple observers or raters are involved. The call to action for researchers is clear: rigorous training and comprehensive guidelines for raters are non-negotiable. Ensuring that raters are well-equipped, both in terms of knowledge and tools, is paramount. It's not just about achieving consistent results; it's about upholding the sanctity of the research process and ensuring that findings are valid and reliable. Future Directions As we look ahead, the landscape of inter-rater reliability is poised for evolution. With technological advancements, there's potential for more sophisticated methods to measure and ensure IRR. Software solutions equipped with artificial intelligence and machine learning capabilities might soon offer tools that can assist in training raters, providing real-time feedback, and even predicting areas of potential disagreement. Moreover, as research methodologies become more intricate, the role of technology in aiding the process of ensuring IRR will undoubtedly grow. The future holds promise, from virtual reality-based training modules for raters to advanced statistical tools that can analyze inter-rater discrepancies in real time. For researchers and professionals alike, staying abreast of these advancements will ensure their work remains at the forefront of reliability and validity. In conclusion, while the principles of inter-rater reliability remain steadfast, the tools and methods to achieve it are ever-evolving, promising a future where consistency in evaluations is not just hoped for but assured.

Sep 01 2023

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Meta AI's CoTracker: It is Better to Track Together for Video Motion Prediction

In deep learning, establishing point correspondences in videos is a fundamental challenge with broad applications. Accurate video motion prediction is crucial for various downstream machine learning tasks, such as object tracking, action recognition, and scene understanding. To address the complexities associated with this task, Meta AI introduces "CoTracker," a cutting-edge architecture designed to revolutionize video motion estimation. CoTracker: It is Better to Track Together Video Motion Estimation Video motion estimation involves predicting the movement of points across frames in a video sequence. Traditionally, two main approaches have been used: optical flow and tracking algorithm. Optical flow estimates the velocity of points within a video frame, while the tracking method focuses on estimating the motion of individual points over an extended period. While both approaches have their strengths, they often overlook the strong correlations between points, particularly when points belong to the same physical object. These correlations are crucial for accurate motion prediction, especially when dealing with occlusions and complex scene dynamics. Video motion estimation has many practical applications in artificial intelligence, enabling enhanced visual understanding and interaction. In surveillance, it aids in object detection and anomaly detection. In filmmaking and entertainment, it drives special effects and scene transitions. In robotics and automation, it enhances robotic movement and task execution. Autonomous vehicles utilize it for environment perception and navigation. Medical imaging can benefit from motion-compensated diagnostics. Virtual reality benefits from realistic movement portrayal. Video compression and streaming utilize motion estimation for efficient data transmission. Co-Tracker: Architecture Meta AI has introduced  "CoTracker," an innovative architecture that enhances video motion prediction by jointly tracking multiple points throughout an entire video sequence. CoTracker is built on the foundation of the transformer network, a powerful and flexible neural architecture that has demonstrated success in various natural language processing and computer vision tasks. The key innovation of CoTracker is its ability to leverage both time and group attention blocks within the transformer architecture. By interleaving these attention blocks, CoTracker achieves a more comprehensive understanding of motion dynamics and correlations between points. This design enables CoTracker to overcome the limitations of traditional methods that focus on tracking points independently, thus unlocking a new era of accuracy and performance in video motion prediction. CoTracker: It is Better to Track Together Transformer Formulation The Co-Tracker architecture utilizes a transformer network with a CNN-based foundation, a versatile and powerful neural network architecture. This network denoted as Ψ : G → O, is tailored to enhance the accuracy of track estimates. Tracks are represented as input tokens Gi, encoding essential information like image features, visibility, appearance, correlation vectors, and positional encodings. The transformer processes these tokens iteratively to refine track predictions, ensuring context assimilation. The optimization of visibility is achieved through learned weights and strategically initialized quantities Windowed Inference Co-Tracker has the ability to support windowed applications, allowing it to efficiently handle long videos. In scenarios where the video length T' exceeds the maximum window size supported by the architecture, the video is split into windows with an overlap. The transformer is then applied iteratively across these windows, allowing the model to process extended video sequences while preserving accuracy. Unrolled Learning Unrolled learning is a vital component of Co-Tracker's training process. This mechanism enables the model to handle semi-overlapping windows, which is essential for maintaining accuracy across longer videos. During training, the model is trained using an unrolled fashion, effectively preparing it to handle videos of varying lengths during evaluation. Transformer Operation and Components Co-Tracker's transformer operates using interleaved time and group attention blocks. This unique approach allows the model to consider temporal and correlated group-based information simultaneously. Time attention captures the evolution of individual tracks over time, while group attention captures correlations between different tracks. This enhances the model's ability to reason about complex motion patterns and occlusions. Point Selection A crucial aspect of Co-Tracker's success lies in its approach to point selection. To ensure a fair comparison with existing methods and to maintain robustness in performance, the model is evaluated using two-point selection strategies: global and local. In the global strategy, points are selected on a regular grid across the entire image. In the local strategy, points are chosen in proximity to the target point. Point selection enhances the model's ability to focus on relevant points and regions, contributing to its accuracy in motion prediction. CoTracker: It is Better to Track Together Co-Tracker: Implementation Co-Tracker's implementation involves rendering 11,000 pre-generated 24-frame sequences from TAP-Vid-Kubric, each annotated with 2,000 tracked points. These points are preferentially sampled on objects.  During training, 256 points are randomly selected per sequence, visible either in the first or middle frames. Co-Tracker is trained as a baseline on TAP-Vid-Kubric sequences of size 24 frames using sliding windows of size 8 frames, iterated 50,000 times on 32 NVIDIA TESLA Volta V100 32GB GPUs. This scalable approach ensures efficient learning and flexibility to adapt the batch size according to available GPU memory, resulting in high-quality tracking performance and achieving a stable frame rate (fps).  Ground truth annotations enhance the training process, contributing to the model's robustness and accuracy in capturing complex motion patterns. To access the model on GitHub, visit: Co-Tracker.   Co-Tracker: Experiments and Benchmarks The Co-Tracker efficacy in video motion prediction and point tracking was evaluated on a series of experiments and benchmark assessments. The performance of the architecture was rigorously tested using a combination of synthetic and real-world datasets, each carefully chosen to represent a spectrum of challenges. The synthetic dataset, TAP-Vid-Kubric, played a pivotal role in training the architecture and simulating dynamic scenarios with object interactions. Benchmark datasets like TAP-Vid-DAVIS, TAP-Vid-Kinetics, BADJA, and FastCapture provided real-world videos with annotated trajectories to facilitate the assessment of Co-Tracker's predictive prowess. These evaluations adhered to predefined protocols tailored to the intricacies of each dataset. The "queried strided" protocol was adopted, requiring precise tracking in both forward and backward directions to address varying motion complexities. Evaluation metrics such as Occlusion Accuracy (OA), Average Jaccard (AJ), and Average Positional Accuracy (< δx avg) were used to gauge the architecture's performance. Co-Tracker: Results CoTracker: It is Better to Track Together The paper explores the impact of joint tracking and support grids, an essential element of Co-Tracker's design. By evaluating different support grids and employing the "uncorrelated single target point" protocol, it demonstrated that the architecture's ability to collectively reason about tracks and their trajectories (group attention and time attention) led to improved outcomes. The best results were achieved when the correct contextual points were considered, highlighting the effectiveness of combining local and global grids. The potential for even better performance was seen when using the "all target points" protocol, indicating that correlated points are indeed influential. Although this protocol was not directly compared to prior work for fairness, it aligns with real-world scenarios where segmentation models could automatically select correlated points. When compared to prior state-of-the-art AI models like RAFT and PIPs, Co-Tracker exhibited remarkable accuracy in tracking points and their visibility across various benchmark datasets. The architecture's capacity for long-term tracking of points in groups was especially beneficial. This approach was different from traditional single-point models and short-term optical flow methods that often grapple with accumulated drift issues. The meticulous evaluation protocol further solidified Co-Tracker's superior predictive capabilities. CoTracker: It is Better to Track Together During the exploration of the importance of training data, TAP-Vid-Kubric emerged as the superior choice vs.  FlyingThings++. The latter's short sequences clashed with Co-Tracker's reliance on sliding windows for training. On the other hand, Kubric's realistic scenes and occluded objects aligned seamlessly with the architecture's design. The significance of unrolled learning in the sliding window scheme was demonstrated through evaluations. Given that evaluation sequences often exceeded training video lengths, Co-Tracker's ability to propagate information between windows emerged as a crucial factor in its exceptional performance. Read the original paper by Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht on Arxiv: CoTracker: It is Better to Track Together.   Co-Tracker: Key Takeaways CoTracker: It is Better to Track Together Group Tracking Boosts Accuracy: Co-Tracker's simultaneous tracking of multiple points improves accuracy by considering correlations between them, surpassing single-point models. Contextual Points Matter: Co-Tracker's success depends on choosing contextual points effectively within support grids, highlighting the importance of context in accurate tracking. Long-Term Group Tracking Prevails: Co-Tracker's long-term group tracking surpasses single-point models and short-term optical flow methods, ensuring better predictive accuracy and mitigating drift issues. Training Data's Influence: TAP-Vid-Kubric's training data is superior, aligning well with Co-Tracker's approach and offering more realistic scenes than FlyingThings++. Efficient Unrolled Learning: Co-Tracker's unrolled learning for sliding windows efficiently propagates information, proving vital for maintaining accuracy on longer sequences. Co-Tracker's success hinges on correlation utilization, context consideration, and real-world adaptability, solidifying its role as a transformative solution for video motion prediction and point tracking.

Aug 30 2023

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Data Curation in Computer Vision

In recent years, the explosion in data volume, which soared to around 97 zettabytes globally in 2022 and is projected to reach over 181 zettabytes by 2025, has been a boon for the fields of artificial intelligence (AI) and machine learning (ML). These fields thrive on large datasets to generate more accurate results. Extracting value from such vast amounts of data, however, is often challenging. High-quality data is necessary to produce good results, particularly for AI systems powered by sophisticated computer vision (CV) algorithms and foundation models. Since CV models typically process unstructured data consisting of thousands of images, managing datasets effectively becomes crucial. One aspect of data management that’s essential is data curation. When you incorporate data curation as part of your workflow, you can check for data quality issues, missing values, data distribution drift, and inconsistencies across your datasets that could impact the performance of downstream computer vision and machine learning models. Data curation also helps you efficiently select edge cases, scenarios that are unlikely but could have significant implications if overlooked. For instance, a model for self-driving cars trained on images of roads and alleys under normal weather conditions may fail to recognize critical objects under extreme weather. Therefore, curating a dataset to include images across a spectrum of weather conditions is essential to account for such edge cases. In this article, you will learn about data curation in detail, explore its challenges, and understand how it helps improve CV model performance. We’ll also specifically discuss the role of data annotation in curating computer vision datasets and how you can use the Encord platform for data curation. Ready? Let’s dive right in! 🏊 What is Data Curation? When you “curate data,” it simply means collecting, cleaning, selecting, and organizing data for your ML models so downstream consumers can get complete, accurate, relevant, and unbiased data for training and validating models. It’s an iterative process that you must follow even after model deployment to ensure the incoming data matches the data in the production environment. Data curation differs from data management—management is a much broader concept involving the development of policies and standards to maintain data integrity throughout the data lifecycle. What are the steps involved in data curation? Let’s explore them below! Data Collection The data curation phase starts with collecting data from disparate sources, public or proprietary databases, data warehouses, or web scraping. Data Validation After collection, you can validate your data using automated pipelines to check for accuracy, completeness, relevance, and consistency. Data Cleaning Then, clean the data to remove corrupted data points, outliers, incorrect formats, duplicates, and other redundancies to maintain data quality. Want to learn more about data cleaning? Read our comprehensive guide on Mastering Data Cleaning and Data Preprocessing.  Normalization Next is normalization, which involves re-scaling data values to a standard range and distribution that aid algorithms that are sensitive to input scales, thus preventing skews in learned weights and coefficients. De-identification It is a standard method of removing personally identifiable information from datasets, such as names, social security numbers (SSNs), and contact information. Data Transformation You can build automated pipelines to transform data into meaningful features for better model training. Feature engineering is a crucial element in this process. It allows data teams to find relevant relationships between different columns and turn them into features that help explain the target variable. Data Augmentation Data augmentation introduces slight dataset variations to expand data volume and scenario coverage. You can use image operations like crop, flip, zoom, rotate, pan, and scale to enhance computer vision datasets. Data augmentation example Note that augmented data differs from synthetic data. Synthetic data is computer-generated fake data that resembles real-world data. Typically, it is generated using state-of-the-art generative algorithms. On the other hand, augmented data refers to variations in training data, regardless of how it is generated. Data Sampling Data sampling refers to using a subset of data to train AI models. However, this may introduce bias during model training since we select only a specific part of the dataset. Such issues can be avoided through probabilistic sampling techniques like random, stratified, weighted, and importance sampling. You can read our complete guide on How to Mitigate Bias in Machine Learning Models to learn how to reduce bias in models efficiently. Data Partitioning The final step in data curation is data partitioning. This involves dividing data into training, validation, and test sets. The model uses the training datasets to learn patterns and compute coefficients or weights. During training, the model’s performance is tested on a validation set. If the model performs poorly during validation, it can be adjusted by fine-tuning its hyper-parameters. Once you have satisfactory performance on the validation set, the test set is used to assess critical performance metrics, such as accuracy, precision, F1 score, etc., to see if the model is ready for deployment. While there’s no one fixed way of splitting data into train, test, and validation sets, you can use the sampling methods described in the previous section to ensure that each dataset represents the population in a balanced manner. Doing so ensures your model doesn’t suffer from underfitting or overfitting. Get a deeper understanding of training, validation, and test sets by reading the article on Training, Validation, Test Split for Machine Learning Datasets. Data Curation in Computer Vision While the above data curation steps generally apply to machine learning, the curation process involves more complexity when preparing data for computer vision tasks. First, let’s list the common types of computer vision tasks and then discuss annotation, a critical data curation step. Common Types of Computer Vision Tasks Object Detection This task is for when you want to identify specific objects within your images. Below is an example of a butterfly detected with bounding boxes around the object, including a classification of the species of the butterfly “Ringlet.” Object detection example in Encord Annotate. Interested in reading more about object detection? Head to our blog to read Object Detection: Models, Use Cases, and Examples. Image Classification Image classification models predict whether an object exists in a given image based on the patterns they learn from the training data. For instance, an animal classifier would label the below image as “Crab” if the classifier had been trained on a good sample of crab images. “Walking Crab” classification in Encord Active. Face Recognition Facial recognition tasks involve complex convolutional neural nets (CNNs) to learn intricate facial patterns and recognize faces in images. Semantic Segmentation You can identify each pixel of a given object within an image through semantic segmentation. For instance, the image below illustrates how semantic segmentation distinguishes between several elements in a given image on a pixel level. Semantic segmentation example Text-to-Image Generative Models Generating images from text is a new development in the generative AI space that involves writing text-based input prompts to describe the type of image you want. The generative model processes the prompt and produces suitable images that match the textual description. Several proprietary and open-source models, such as Midjourney, Stable Diffusion, Craiyon, DeepFloyd, etc., are recent examples that can create realistic photos and artwork in seconds. Role of Data Annotation In Curating Computer Vision Data Computer vision tasks require careful data annotation as part of the data curation process to ensure that models work as expected. Data annotation refers to labeling images (typically in the training data) so the model knows the ground truth for accurate predictions. Let’s explore a few annotation techniques below. Bounding Box: The technique annotates a bounding box around the object of interest for image classification and object detection tasks. An example of bounding box annotation within Encord Annotate. Landmarking: In landmarking, the objective is to annotate individual features within an image. It’s suitable for facial recognition tasks. An example of landmarking to label different facial features Tracking: Tracking is useful for annotating moving objects across multiple images. An example of tracking a moving car label within Encord. General Considerations for Annotating Image Data Data annotation can be time-consuming as it requires considerable effort to label each image or object within an image. It’s advisable to clearly define standard naming conventions for labeling to ensure consistency across all images. You can use labeled data from large datasets, such as ImageNet, which contains over a million training images across 1,000 object classes. It is ideal for building a general-purpose image classification model. Also, it’s important to develop a robust review process to identify annotation errors before feeding the data to a CV model. Leveraging automation in the annotation workflow reduces the time to identify those errors, as manual review processes are often error-prone and costly. Moreover, your team can employ innovative methods like active learning and image embeddings to improve data annotation accuracy. Let’s look at them briefly below. Active Learning Instead of labeling all the images in a dataset, an active learning workflow allows you to annotate only a few valuable images and use them for training. It uses an informativeness score that helps decide which image will be most beneficial to improving performance. For example, in a given dataset containing 1,500 images, the active learning method identifies the most valuable data samples (let’s say 100 images) for annotation, allowing you to train your ML model on a subset of labeled images and validate it on the remaining unlabeled 1,400 images. Metric performance explorer in Encord Active. You can use a data and model evaluation tool like Encord Active to assign confidence scores to the 1,400 images and send them upstream for data annotators on Encord Annotate to cross-check images with the lowest scores and re-label them manually. Through this, active learning reduces data annotation time and can significantly improve the performance of your computer vision models. Interested in learning more about active learning? Read our detailed Practical Guide to Active Learning for Computer Vision Image Embeddings Image embeddings are vectorized versions of image data where similar images have similar numerical vector representations. Data embeddings plot in Encord Active. Typically, image embeddings are helpful for semantic segmentation tasks as they break down an image into relevant vectors, allowing computer vision models to classify pixels more accurately. They also help with facial recognition tasks by representing each facial feature as a number in the vector. The model can better use the vectorized form to distinguish between several facial structures. Embeddings make it easier for algorithms to compute how similar two or more images are numerically. It helps practitioners annotate images more accurately. Lastly, image embeddings are the backbone of generative text-to-image models, where practitioners can convert text-image pairs into embeddings. For example, you can have the text “image of a dog” and an actual dog’s image paired together and converted into an embedding. You can pass such embeddings as input to a generative model so it learns to create a dog’s image when it identifies the word “Dog” in a textual prompt. Challenges in Data Curation Data provides the foundation for building high-quality machine learning models. However, collecting relevant data comes with several challenges. Evolving Data Landscape: With the rapid rise of big data, maintaining consistent and accurate data across time and platforms is challenging. Data distributions can change quickly as more data comes in, making data curation more difficult. Data Security Concerns: Edge computing is giving rise to security issues as organizations must ensure data collection from several sources is secure. It calls for robust encryption and de-identification strategies to protect private information and maintain data integrity throughout curation. Data Infrastructure and Scalability: It’s difficult for organizations to develop infrastructure for handling the ever-increasing scale of data and ML applications. The exponential rise in data volume is causing experts to shift from code-based strategies to data-centric AI, primarily focusing on building models that help with data exploration and analysis. Data Scarcity: Mission-critical domains like healthcare often need more high-quality data sources. This makes it difficult for you to curate data and build accurate models. Models built using low-quality data can more likely give false positives, which is why expert human supervision is required to monitor the outcomes of such models. Using Encord Active for Data Curation Encord’s end-to-end training data platform enables you to curate and manage data. The platform has features for finding labeling errors quickly through vector embeddings, AI-assisted metrics, and model predictions. You can build robust active learning pipelines to speed up the data curation process. In this section, you will see how the different stages of the data curation workflow work in Encord. Data Annotation and Validation After collecting your dataset, you need to annotate it and validate the quality of your annotations and images. For this stage, Encord Annotate supports all key annotation types, such as bounding boxes, polygons, polylines, image segmentation, and more, across various visual formats. Polygon and bounding box annotations in Encord Annotate. It includes auto-annotation features such as Meta’s Segment Anything Model and other AI-assisted labeling techniques that can aid your annotation process and reduce the chances of annotation errors occurring. Annotate provides data integrations into popular storage services and warehouses, so you do not have to worry about moving your data. Annotate’s quality assessment toolkit helps scale your data validation processes by spotting hidden errors in your training dataset. You can also use Annotate to automatically find classification and geometric errors in your training data, ensuring that your labels are of the highest possible quality before they go into production. The illustration below shows what you can expect the annotation and validation workflows to look like with Encord: Annotation and validation workflow with Encord. Data Cleaning With Encord Active, you can refine the dataset by efficiently identifying and removing duplicate entries, for example. How does it do this? Active computes the image embeddings and uses algorithms to evaluate the dataset based on objective quality metrics like “Uniqueness,” “Area,” “Contrast,” and so on. Try Below In this example, you will identify outlier images using the embeddings view, use the "similarity search" to find similar objects, select multiple images and then add to a collection. Normalization While curating your data, you might want to adjust your values on a consistent scale. In color images, each pixel has three values (one for each of the red, green, and blue channels), usually ranging from 0 to 255. Normalization rescales these pixel values to a new range. Exploring your data distribution provides a clear lens to understand the images you want to normalize to a standard range, often 0 to 1 or -1 to 1. In the workflow below, you can see the distribution of Red, Blue, and Green pixel values across an entire image set. Metric distribution in Encord Active. De-identification Safeguarding sensitive information is fundamental to building trust and ensuring the ethical use of data in machine learning and computer vision applications. Active can aid the de-identification process by allowing you to identify images through textual prompts that likely contain Personally Identifiable Information (PII). Annotate can help you anonymize or de-identify PII programmable from the SDK. Finding human faces in Encord Active. Data curation is a critical determinant of the success of computer vision projects as businesses increasingly depend on AI for better user applications and efficient business operations.  However, the complexity and challenges of data curation, especially in computer vision, call for the right tools to streamline this process. The Encord platform provides the tools to curate and manage your data pipelines. Data Curation: Key Takeaways As companies gravitate more toward AI to solve business problems using complex data, the importance of data curation will increase significantly. The points below are critical considerations organizations must make to build a successful data curation workflow. Data curation is a part of data management. As such, data curation in isolation may only solve a part of the problem. Companies must have a holistic management policy and adopt the proper workflows to ensure curation yields value. The curation workflow must suit specific requirements. A workflow that works for a particular task may fail to produce results for another. Encord allows you to customize and automate your data curation workflow for any vision use case. The right combination of data curation tools can accelerate the development of high-quality training data. Encord Annotate provides features to label visual data and manage large-scale annotation teams using customizable workflows and quality control tools. With Encord Active, you can find failure modes, surface poor-quality data, and evaluate your model’s performance. Data curation is an ongoing process, and each organization must commit to robust data curation practices throughout the model building, deployment, and monitoring stages while continuing to improve the curation workflow as data evolves.

Aug 24 2023

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Fine-tuning Models: Hyperparameter Optimization

Hyperparameter optimization is a key concept in machine learning. At its core, it involves systematically exploring the most suitable set of hyperparameters that can elevate the performance of a model. These hyperparameters, distinct from model parameters, aren't inherently learned during the training phase. Instead, they're predetermined. Their precise configuration can profoundly sway the model's outcome, bridging the gap between an average model and one that excels. Fine-tuning models delves into the meticulous process of refining a pre-trained model to better align with a specific task. Imagine the precision required in adjusting a musical instrument to hit the right notes; that's what fine-tuning achieves for models. It ensures they resonate perfectly with the data they're presented. The model learns at its maximum potential when hyperparameter optimization and fine-tuning converge. This union guarantees that machine learning models function and thrive, delivering unparalleled performance. The role of tools like the Adam optimizer in this journey cannot be understated. As one of the many techniques in the hyperparameter optimization toolkit, it exemplifies the advancements in the field, offering efficient and effective ways to fine-tune models to perfection. This article will cover: What is Hyperparameter Optimization? Techniques for Hyperparameter Optimization. The Role of Adam Optimizer  Challenges in Hyperparameter Optimization. Diagram illustrating hyperparameter optimization process What is Hyperparameter Optimization? With its vast potential and intricate mechanisms, machine learning often hinges on fine details. One such detail pivotal to the success of a model is hyperparameter optimization. At its core, this process systematically searches for the best set of hyperparameters to elevate a model's performance.  But what distinguishes hyperparameters from model parameters? Model parameters are the model's aspects learned from the data during training, such as weights in a neural network. Hyperparameters, on the other hand, are set before training begins. They dictate the overarching structure and behavior of a model. They are adjusted settings or dials to optimize the learning process. This includes the learning rate, which determines how quickly a model updates its parameters in response to the training data, or the regularization term, which helps prevent overfitting.4 The challenge of hyperparameter optimization is monumental. Given the vastness of the hyperparameter space, with an almost infinite number of combinations, finding the optimal set is like searching for a needle in a haystack.  Techniques such as grid search, where a predefined set of hyperparameters is exhaustively tried, or random search, where hyperparameters are randomly sampled, are often employed. More advanced methods like Bayesian optimization, which builds a probabilistic model of the function mapping from hyperparameter values to the objective value, are also gaining traction.5 Why Fine-tuning is Essential The configuration and hyperparameter tuning can profoundly influence a model's performance. A slight tweak can be the difference between a mediocre outcome and stellar results. For instance, the Adam optimizer, a popular **optimization method** in deep learning, has specific hyperparameters that, when fine-tuned, can lead to faster and more stable convergence during training. 6 In real-world applications, hyperparameter search and fine-tuning become even more evident. Consider a scenario where a pre-trained neural network, initially designed for generic image recognition, is repurposed for a specialized task like medical image analysis. Its accuracy and reliability can be significantly enhanced by searching for optimal hyperparameters and fine-tuning them for this dataset. This could mean distinguishing between accurately detecting a medical anomaly and missing it altogether. Furthermore, as machine learning evolves, our datasets and challenges become more complex. In such a landscape, the ability to fine-tune models and optimize hyperparameters using various optimization methods is not just beneficial; it's essential. It ensures that our models are accurate, efficient, adaptable, and ready to tackle the challenges of tomorrow. Techniques for Hyperparameter Optimization Hyperparameter optimization focuses on finding the optimal set of hyperparameters for a given model. Unlike model parameters, these hyperparameters are not learned during training but are set before the training begins. Their correct setting can significantly influence the model's performance. Grid Search Grid Search involves exhaustively trying out every possible combination of hyperparameters in a predefined search space. For instance, if you're fine-tuning a model and considering two hyperparameters, learning rate and batch size, a grid search would test all combinations of the values you specify for these hyperparameters. Let's consider classifying images of handwritten digits (a classic problem known as the MNIST classification). Here, the images are 28x28 pixels, and the goal is to classify them into one of the ten classes (0 through 9). For an SVM applied to this problem, two critical hyperparameters are: The type and parameters of the kernel: For instance, if using the Radial Basis Function (RBF) kernel, we need to determine the gamma value. The regularization parameter (C) determines the trade-off between maximizing the margin and minimizing classification error. Using grid search, we can systematically explore combinations of: Different kernels: linear, polynomial, RBF, etc. Various values of gamma (for RBF): e.g., [0.1, 1, 10, 100] Different values of C: e.g., [0.1, 1, 10, 100] By training the SVM with each combination and validating its performance on a separate dataset, grid search allows us to pinpoint the combination that yields the best classification accuracy. Advantages of Grid Search Comprehensive: Since it tests all possible combinations, there's a high chance of finding the optimal set. Simple to implement: It doesn't require complex algorithms or techniques. Disadvantages of Grid Search Computationally expensive: As the number of hyperparameters or their potential values increases, the number of combinations to test grows exponentially. Time-consuming: Due to its exhaustive nature, it can be slow, especially with large datasets or complex models. Random Search Random Search, as the name suggests, involves randomly selecting and evaluating combinations of hyperparameters. Unlike Grid Search, which exhaustively tries every possible combination, Random Search samples a predefined number of combinations from a specified distribution for each hyperparameter. 11  Consider a scenario where a financial institution develops a machine learning model to predict loan defaults. The dataset is vast, with numerous features ranging from a person's credit history to current financial status.  The model in question, a deep neural network, has several hyperparameters like learning rate, batch size, and the number of layers. Given the high dimensionality of the hyperparameter space, using Grid Search might be computationally expensive and time-consuming. By randomly sampling hyperparameter combinations, the institution can efficiently narrow down the best settings with the highest prediction accuracy, saving time and computational resources.13 Advantages of Random Search Efficiency: Random Search can be more efficient than Grid Search, especially when the number of hyperparameters is large. It doesn't need to try every combination, which can save time.12 Flexibility: It allows for a more flexible specification of hyperparameters, as they can be drawn from any distribution, not just a grid. Surprising Results: Sometimes, Random Search can stumble upon hyperparameter combinations that might be overlooked in a more structured search approach. Disadvantages of Random Search No Guarantee: There's no guarantee that Random Search will find the optimal combination of hyperparameters, especially if the number of iterations is too low. Dependence on Iterations: The effectiveness of Random Search is highly dependent on the number of iterations. Too few iterations might miss the optimal settings, while too many can be computationally expensive. Bayesian Optimization Bayesian Optimization is a probabilistic model-based optimization technique particularly suited for optimizing expensive-to-evaluate and noisy functions. Unlike random or grid search, Bayesian Optimization builds a probabilistic model of the objective function. It uses it to select the most promising hyperparameters to evaluate the true objective function. Bayesian Optimization shines in scenarios where the objective function is expensive to evaluate. For instance, training a model with a particular set of hyperparameters in deep learning can be time-consuming. Using grid search or random search in such scenarios can be computationally prohibitive. By building a model of the objective function, Bayesian Optimization can more intelligently sample the hyperparameter space to find the optimal set in fewer evaluations. Bayesian Optimization is more directed than grid search, which exhaustively tries every combination of hyperparameters, or random search, which samples them randomly. It uses past evaluation results to choose the next set of hyperparameters to evaluate. This makes it particularly useful when evaluating the objective function (like training a deep learning model) is time-consuming or expensive. However, it's worth noting that if the probabilistic model's assumptions do not align well with the true objective function, Bayesian Optimization might not perform as well. A more naive approach like random search might outperform it in such cases.1 Advantages of Bayesian Optimization Efficiency: Bayesian Optimization typically requires fewer function evaluations than random or grid search, making it especially useful for optimizing expensive functions. Incorporation of Prior Belief: It can incorporate prior beliefs about the function and then sequentially refine this model as more samples are collected. Handling of Noisy Objective Functions: It can handle noisy objective functions, meaning that there's some random noise added to the function's output each time it's evaluated. Disadvantages of Bayesian Optimization Model Assumptions: The performance of Bayesian Optimization can be sensitive to the assumptions made by the probabilistic model. Computationally Intensive: As the number of observations grows, the computational complexity of updating the probabilistic model and selecting the next sample point can become prohibitive. A comparison chart of different optimization techniques The Role of Adam Optimizer Hyperparameters The Adam optimizer has emerged as a popular choice for training deep learning models in the vast landscape of optimization algorithms. But what makes it so special? And how do its hyperparameters influence the fine-tuning process? Introduction to the Adam Optimizer The Adam optimizer, short for Adaptive Moment Estimation, is an optimization algorithm for training neural networks. It combines two other popular optimization techniques: AdaGrad and RMSProp. The beauty of Adam is that it maintains separate learning rates for each parameter and adjusts them during training. This adaptability makes it particularly effective for problems with sparse gradients, such as natural language processing tasks.5 Significance of Adam in Model Training Adam has gained popularity due to its efficiency and relatively low memory requirements. Unlike traditional gradient descent, which maintains a single learning rate for all weight updates, Adam computes adaptive learning rates for each parameter. This means it can fine-tune models faster and often achieve better performance on test datasets. Moreover, Adam is less sensitive to hyperparameter settings, making it a more forgiving choice for those new to model training.. 7 Impact of Adam's Hyperparameters on Model Training The Adam optimizer has three primary hyperparameters: the learning rate, beta1, and beta2. Let's break down their roles: Learning Rate (α): This hyperparameter determines the step size at each iteration while moving towards a minimum in the loss function. A smaller learning rate might converge slowly, while a larger one might overshoot the minimum. Beta1: This hyperparameter controls the exponential decay rate for the first-moment estimate. It's essentially a moving average of the gradients. A common value for beta1 is 0.9, which means the algorithm retains 90% of the previous gradient's value.8 Beta2 controls the exponential decay rate for the second moment estimate, an uncentered moving average of the squared gradient. A typical value is 0.999. 8 Fine-tuning these hyperparameters can significantly impact model training. For instance, adjusting the learning rate can speed up convergence or prevent the model from converging. Similarly, tweaking beta1 and beta2 values can influence how aggressively the model updates its weights in response to the gradients. Practical Tips for Fine-tuning with Adam Start with a Smaller Learning Rate: While Adam adjusts the learning rate for each parameter, starting with a smaller global learning rate (e.g., 0.0001) can lead to more stable convergence, especially in the early stages of training. Adjust Beta Values: The default values of beta1 = 0.9 and beta2 = 0.999 work well for many tasks. However, slightly adjusting specific datasets or model architectures can lead to faster convergence or better generalization. Monitor Validation Loss: Always monitor your validation loss. If it starts increasing while the training loss continues to decrease, it might be a sign of overfitting. Consider using early stopping or adjusting your learning rate. Warm-up Learning Rate: Gradually increasing the learning rate at the beginning of training can help stabilize the optimizer. This "warm-up" phase can prevent large weight updates that can destabilize the model early on. Use Weight Decay: Regularization techniques like weight decay can help prevent overfitting, especially when training larger models or when the dataset is small. Epsilon Value: While the default value of ε is usually sufficient, increasing it slightly can help with numerical stability in some cases. Best Practices Learning Rate Scheduling: Decreasing the learning rate as training can help achieve better convergence. Techniques like step decay or exponential decay can be beneficial. Batch Normalization: Using batch normalization layers in your neural network can make the model less sensitive to the initialization of weights, aiding in faster and more stable training. Gradient Clipping: For tasks like training RNNs, where gradients can explode, consider gradient clipping to prevent substantial weight updates. Regular Checkpoints: Always save model checkpoints regularly. This helps in unexpected interruptions and allows you to revert to a previous state if overfitting occurs. Adam optimizer is powerful and adaptive; understanding its intricacies and fine-tuning its hyperparameters can improve model performance. Following these practical tips and best practices ensures that your model trains efficiently and generalizes well to unseen data. Visualization of Adam Optimizer in action Challenges in Hyperparameter Optimization Let's delve into common pitfalls practitioners face while choosing the best hyperparameters and explore strategies to overcome them. The Curse of Dimensionality When dealing with many hyperparameters, the search space grows exponentially. This phenomenon, known as the curse of dimensionality, can make the optimization process computationally expensive and time-consuming. 9  Strategy: One way to tackle this is by using dimensionality reduction techniques or prioritizing the most impactful hyperparameters. Local Minima and Plateaus Optimization algorithms can sometimes get stuck in local minima or plateaus, where further adjustments to hyperparameters don't significantly improve performance. 10 Strategy: Techniques like random restarts, where the optimization process is started from different initial points, or using more advanced optimization algorithms like Bayesian optimization, can help navigate these challenges. Overfitting Strategy: Regularization techniques, cross-validation, and maintaining a separate validation set can help prevent overfitting during hyperparameter optimization. For a deeper dive into data splitting techniques crucial for segregating the training set and test set, and playing a pivotal role in model training and validation, check out our detailed article on Train-Validation-Test Split. Computational Constraints Hyperparameter optimization, especially grid search, can be computationally intensive. This becomes a challenge when resources are limited. Strategy: Opt for more efficient search methods like random or gradient-based optimization, which can provide good results with less computational effort. Lack of Clarity on Which Hyperparameters to Tune Strategy: Start with the most commonly tuned hyperparameters. For instance, when fine-tuning models using the Adam optimizer, focus on learning and decay rates. 9 Hyperparameter optimization is essential for achieving the best model performance, and awareness of its challenges is crucial. By understanding these challenges and employing the strategies mentioned, practitioners can navigate the optimization process more effectively and efficiently. Overfitting and Regularization The Balance Between Model Complexity and Generalization A model's complexity is directly related to its number of parameters. While a more complex model can capture intricate patterns in the training data, it's also more susceptible to overfitting.4 Conversely, a too-simple model might not capture the necessary patterns, leading to underfitting. The challenge lies in finding the sweet spot where the model is complex enough to learn from the training data but not so much that it loses its generalization ability. Role of Hyperparameters in Preventing Overfitting Hyperparameters can significantly influence a model's complexity. For instance, the number of layers and nodes in neural networks can determine how intricate patterns the model can capture.9 However, we can fine-tune this balance with the Adam optimizer and its hyperparameters. The learning rate, one of the primary hyperparameters of the Adam optimizer, determines the step size at each iteration while moving towards a minimum of the loss function. A lower learning rate might make the model converge slowly, but it can also help avoid overshooting and overfitting. On the other hand, a larger learning rate might speed up the convergence. Still, it can cause the model to miss the optimal solution. 9 Regularization techniques, like L1 and L2 regularization, add a penalty to the loss function. By adjusting the regularization hyperparameter, one can control the trade-off between fitting the training data closely and keeping the model weights small to prevent overfitting. Graph illustrating overfitting and the role of hyperparameters Hyperparameter Optimization: Key Takeaways In the intricate landscape of machine learning, hyperparameter tuning and hyperparameter search are essential processes, ensuring that models achieve optimal performance through meticulous fine-tuning. The balance between model complexity and its generalization capability is paramount. The role of hyperparameters, especially within the framework of the Adam optimizer, is pivotal in maintaining this equilibrium and finding the optimal hyperparameters. As machine learning continues to evolve, practitioners must remain aware of evolving methodologies and optimization methods. The hyperparameter optimization process is not a mere task but an ongoing commitment to refining models for superior outcomes. It is, therefore, incumbent upon professionals in this domain to engage in rigorous experimentation and continual learning, ensuring that the models they develop are efficient, robust, and adaptable to the ever-evolving challenges presented by real-world data.

Aug 22 2023

sampleImage_evaluate-foundation-model-clip-with-encord-active-part2

Part 2: Evaluating Foundation Models (CLIP) using Encord Active

In the first article of this series on evaluating foundation models using Encord Active, you applied a CLIP model to a dataset that contains images of different facial expressions. You also saw how you could generate the classifications for the facial expressions using the CLIP model and import the predictions into Encord Active.  To round up that installment, you saw how Encord Active can help you evaluate your model quality by providing a handy toolbox to home in on how your model performs on different subsets of data and metrics (such as image singularity, redness, brightness, blurriness, and so on). In this installment, you will focus on training a CNN model on the ground truth labels generated by the CLIP model. Toward the end of the article, you will import the dataset, ground truth labels, and model into Encord Active to evaluate the model and interpret the results to analyze the quality of your model. Let’s jump right in! 🚀 Train CNN Model on A Dataset with Ground Truth Labels In this section, you will train a CNN on the dataset created from labels predicted by the CLIP model. We saved the name of the dataset folder as Clip_GT_labels in the root directory—the code snippet for creating the new dataset from the CLIP predictions. Remember to check out the complete code for the article in this repository. Create a new Python script named “train_cnn.py” in the root directory. Import the required libraries: import torch import torch.nn as nn import torch.optim as optim import torchvision.transforms as transforms import torchvision.datasets as datasets from torch.utils.data import DataLoader from torch.autograd import Variable from tqdm import tqdm Next, define transforms for data augmentation and load the dataset: # Define the data transformations train_transforms = transforms.Compose([ transforms.Resize((256, 256)), transforms.RandomHorizontalFlip(), transforms.RandomVerticalFlip(), transforms.ToTensor(), ]) val_transforms = transforms.Compose([ transforms.Resize((256, 256)), transforms.ToTensor(), ]) # Load the datasets train_dataset = datasets.ImageFolder( r'Clip_GT_labels\Train', transform=train_transforms ) val_dataset = datasets.ImageFolder( r'Clip_GT_labels\Val', transform=val_transforms ) # Create the data loaders train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False) Next, define the CNN architecture, initialize the model, and define the loss function and optimizer: # Define the CNN architecture class CNN(nn.Module): def __init__(self, num_classes=7):     super(CNN, self).__init__()     # input shape (3, 256, 256)     self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)     self.relu1 = nn.ReLU(inplace=True)     self.pool1 = nn.MaxPool2d(kernel_size=2)     # shape (16, 128, 128)     # input shape (16, 128, 128)     self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)     self.relu2 = nn.ReLU(inplace=True)     self.pool2 = nn.MaxPool2d(kernel_size=2)     # output shape (32, 64, 64)     # input shape (32, 64, 64)     self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)     self.relu3 = nn.ReLU(inplace=True)     self.pool3 = nn.MaxPool2d(kernel_size=2)     # output shape (64, 32, 32)     # input shape (64, 32, 32)     self.conv4 = nn.Conv2d(64, 32, kernel_size=3, padding=1)     self.relu4 = nn.ReLU(inplace=True)     self.pool4 = nn.MaxPool2d(kernel_size=2)     # output shape (32, 16, 16)     self.fc1 = nn.Linear(32 * 16 * 16, 128)     self.relu5 = nn.ReLU(inplace=True)     self.dropout = nn.Dropout(0.5)     self.fc2 = nn.Linear(128, num_classes) def forward(self, x):     x = self.conv1(x)     x = self.relu1(x)     x = self.pool1(x)     x = self.conv2(x)     x = self.relu2(x)     x = self.pool2(x)     x = self.conv3(x)     x = self.relu3(x)     x = self.pool3(x)     x = self.conv4(x)     x = self.relu4(x)     x = self.pool4(x)     x = x.view(-1, 32 * 16 * 16)     x = self.fc1(x)     x = self.relu5(x)     x = self.dropout(x)     x = self.fc2(x)     return x # Initialize the model and define the loss function and optimizer model = CNN() criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) Finally, here’s the code to train the CNN on the dataset and export the model: # Train the model device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model.to(device) num_epochs = 50 best_acc = 0.0 for epoch in range(num_epochs):     train_loss = 0.0     train_acc = 0.0     model.train()     for images, labels in train_loader:         images = Variable(images.to(device))         labels = Variable(labels.to(device))         optimizer.zero_grad()         outputs = model(images)         loss = criterion(outputs, labels)         loss.backward()         optimizer.step()         train_loss += loss.item() * images.size(0)         _, preds = torch.max(outputs, 1)         train_acc += torch.sum(preds == labels.data)     train_loss = train_loss / len(train_dataset)     train_acc = train_acc / len(train_dataset)     val_loss = 0.0     val_acc = 0.0     model.eval()     with torch.no_grad():         for images, labels in val_loader:             images = images.to(device)             labels = labels.to(device)             outputs = model(images)             loss = criterion(outputs, labels)             val_loss += loss.item() * images.size(0)             _, preds = torch.max(outputs, 1)             val_acc += torch.sum(preds == labels.data)         val_loss = val_loss / len(val_dataset)         val_acc = val_acc / len(val_dataset)     print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Val Loss: {:.4f}, Val Acc: {:.4f}'.format(epoch+1, num_epochs, train_loss, train_acc, val_loss, val_acc))     if val_acc > best_acc:         best_acc = val_acc torch.save(model.state_dict(), 'cnn_model.pth') Now, execute the script: # Go back to root folder cd .. # execute script python train_cnn.py If the script executes successfully, you should see the exported model in your root directory: ├── Clip_GT_labels ├── EAemotions ├── classification_transformer.py ├── cnn_model.pth ├── emotions ├── make_clip_predictions.py └── train_cnn.py Evaluate CNN Model Using Encord Active In this section, you will perform the following task: Create a new Encord project using the test set in the Clip_GT_labels dataset. Load the trained CNN model above (“cnn_model.pth”) and use it to make predictions on the test. Import the predictions into Encord for evaluation. Create An Encord Project Just as you initially created a project in the first article, use the test set in the Clip_GT_labels dataset to initialize a new Encord project. The name specified here for the new project is EAsota. # Create project encord-active init --name EAsota --transformer classification_transformer.py Clip_GT_labels\Test # Change to project directory cd EAsota # Store ontology encord-active print --json ontology Make Predictions using CNN Model In the root directory, create a Python script with the name cnn_prediction.py. Load the new project into the script: # Import encord project project_path = r'EASOTA' project = Project(Path(project_path)).load() project_ontology = json.loads( (project.file_structure.project_dir/'ontology_output.json').read_text() ) ontology = json.loads( project.file_structure.ontology.read_text(encoding="utf-8") ) Next, instantiate the CNN model and load the artifact (saved state): # Create an instance of the model model = CNN() # Load the saved state dictionary file model_path = 'cnn_model.pth' model.load_state_dict(torch.load(model_path)) Using the same procedures as in the previous article, make predictions on the test images and export the predictions by appending them to the predictions_to_import list: model.eval() output = model(image_transformed.to(device).unsqueeze(dim=0)) class_id = output.argmax(dim=1, keepdim=True)[0][0].item() model_prediction = project_ontology['classifications'][classes[class_id]] my_predictions.append(classes[class_id]) confidence = output.softmax(1).tolist()[0][class_id] If you included the same custom metrics, you should have an output in your console: Import Predictions into Encord In the EAsota project, you should find the predictions.pkl file, which stores the predictions from the CNN model.  Import the predictions into Encord Active for evaluation: # Change to Project directory cd ./EAsota # Import Predictions encord-active import predictions predictions.pkl # Start encord-active webapp server encord-active visualize Below is Encord Active’s evaluation of the CNN model’s performance: Interpreting the model's results The classification metrics provided show that the model is performing poorly. The accuracy of 0.27 means that only 27% of the predictions are correct. The mean precision of 0.18 indicates that only 18% of the positive predictions are correct, and the mean recall of 0.23 means that only 23% of the instances belonging to a class are captured.  The mean F1 score of 0.19 reflects the overall balance between precision and recall, but it is still low. These metrics suggest that the model is not making accurate predictions and needs significant improvement.  Encord also visualized each metric's relative importance and correlation to the model's performance. For example, increasing the image-level annotation quality (P), slightly reducing the brightness of the images in the dataset, etc., can positively impact the model’s performance. What have you learned in this series? Over the past two articles, you have seen how to use a CLIP model and train a CNN model for image classification. Most importantly, you learned to use Encord Active, an open-source computer vision toolkit, to evaluate the model’s performance using an interactive user interface. You could also visually get the accuracy, precision, f1-score, recall, confusion matrix, feature importance, etc., from Encord Aactive.  Check out the Encord Active documentation to explore other functionalities of the open-source framework for computer vision model testing, evaluation, and validation. Check out the project on GitHub, leave a star 🌟 if you like it, or leave an issue if you find something is missing—we love feedback!

sampleImage_evaluate-foundation-model-clip-with-encord-active-part1

Part 1: Evaluating Foundation Models (CLIP) using Encord Active

Foundation models (FMs) are new waves of artificial intelligence (AI) models you train on massive loads of unlabeled data, such as images and texts. As a result, you could use FMs on a wide range of tasks, including image classification, natural language processing, code generation, etc., with minimal fine-tuning.  CLIP (Contrastive Language-Image Pre-Training) is a foundational model trained on a massive dataset of image and text pairs. You can use natural language instructions to guide the model in predicting the most relevant text snippet related to an image without precisely fine-tuning it for that particular task. It is similar to the zero-shot capabilities observed in GPT-2 and GPT-3. Encord Active is an open-source toolkit for active learning that enables you to identify failure patterns in your models and enhance both the quality of your data and the performance of your models. Leveraging the capabilities of Encord Active, you can visualize your data, assess your models to uncover instances where models fail, detect labeling errors, prioritize valuable data for re-labeling, and so on. However, it is essential to know that FMs can be inaccurate and biased. In this two-part series, you will use the predictions from CLIP to train a convolutional neural network (CNN) and then use Encord Active to evaluate the performance. In the first part of the series (this read), you will: Download a dataset that contains images of different facial expressions, split it into train/validation/test splits, apply the CLIP model to get predictions, and turn them into ground truth labels. Import the dataset and CLIP model for image-text classification predictions into Encord Active for evaluation. In the second article, you will: Train a CNN model on the ground truth labels from the CLIP model. Import the data, ground truth labels, and model into Encord Active and evaluate the model. Let’s jump right in! 🚀 🔥 NEW RELEASE: We released TTI-Eval (text-to-image evaluation), an open-source library for evaluating zero-shot classification models like CLIP and domain-specific ones like BioCLIP against your (or HF) datasets to estimate how well the model will perform. Get started with it on GitHub, and do ⭐️ the repo if it's awesome. 🔥 Evaluate a CLIP Model on A Dataset with Ground Truth Labels In this section, you will use a CLIP model to classify a dataset with images of different facial expressions. You will approach this task by following the steps below: Set up a Python environment for `encord active` Download the dataset and create an Encord Project Make predictions with CLIP Import predictions into Encord Active for evaluation See the complete code for the article in this repository. Set up A Python Environment for Encord Active Encord Active requires a Python version of 3.9, 3.10, or 3.11. A small request 💜: we’d love to get Encord Active to 1,000 ✨s; consider supporting the library by leaving a ⭐ on the repo. Now, back to the setup ➡️ Run the code below on your command line to set up a Python virtual environment and install the encord-active library. python3.9 -m venv ea-venv # On Linux/MacOS source ea-venv/bin/activate # On Windows ea-venv\Scripts\activate # Install encord-active library python -m pip install encord-active==0.1.69 Next, install the CLIP library from this repository alongside the `tqdm` library to help you monitor the task's progress. # Install tqdm python -m pip install tqdm # Install CLIP python -m pip install git+https://github.com/openai/CLIP.git Download Dataset and Create an Encord Project You will use a dataset that contains images of different facial expressions, including:  anger,  disgust,  fear,  happiness,  neutrality,  sadness,  and surprise.  You can find and download the dataset here. Create a directory; the directory will serve as the root folder for this task. Move the downloaded dataset to the root folder and unzip it using the command below: unzip emotions.zip Creating an Encord Project starts with importing the dataset. Next, run the shell command below from your root directory: encord-active init --name EAemotions ./emotions The name flag specifies the custom name of the project. Assuming the dataset provided is not labeled, you can use the “transformer” option to reference a Python script that defines how to parse the labels. Here is an example of inferring classification for the image dataset. # classification_transformer.py from pathlib import Path from typing import List from encord_active.lib.labels.label_transformer import ( ClassificationLabel, DataLabel, LabelTransformer, ) class ClassificationTransformer(LabelTransformer): def from_custom_labels(self, _, data_files: List[Path]) -> List[DataLabel]:     return [         DataLabel(f, ClassificationLabel(class_=f.parent.name))         for f in data_files     ] To learn more about importing data into Encord, you can read the official Encord Active documentation. After creating the encord-active project with the encord-active init command above, you should have a folder - “EAemotions” created in your root directory. If everything works fine, your root directory tree should look like this: . ├── EAemotions │   ├── data │   ├── embeddings │   ├── image_data_unit.json │   ├── label_row_meta.json │   ├── metrics │   ├── ontology.json │   └── project_meta.yaml ├── classification_transformer.py └── emotions ├── angry ├── disgust ├── fear ├── happy ├── neutral ├── sad └── surprise 12 directories, 5 files Make and Import CLIP Model Predictions into Encord-Active Project In this section, you will use the CLIP model to classify the image dataset, and next, you will import the predictions into encord-active. When preparing predictions for import, keeping track of the class_id of each prediction is very important. The class_id informs encord-active of the class to which each prediction belongs. The class_ids of an Encord project are defined by the featureNodeHash attribute on objects in the Encord ontology.  Export the class names and class_ids in the encord-active project: # Change Directory cd ./EAemotions # Store ontology encord-active print --json ontology You should find a newly created JSON file, ontology_output.json, in the “EAemotions” directory. . ├── EAemotions │   ├── data │   ├── embeddings │   ├── image_data_unit.json │   ├── label_row_meta.json  │   ├── metrics │   ├── ontology.json │   ├── ontology_output.json │   └── project_meta.yaml    ├── classification_transformer.py └── emotions     ├── angry ├── disgust ├── fear      ├── happy     ├── neutral ├── sad     └── surprise 12 directories, 6 files Image Classification Using CLIP Foundational Model In your root directory, create a Python script with the name make_clip_predictions.py (you can use any custom file name).  Import the required libraries and define some important variables. # make_clip_predictions.py import json import os import pickle from pathlib import Path import shutil import cv2 import clip import matplotlib.pyplot as plt import numpy as np from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix import torch from torchvision import transforms from tqdm import tqdm from encord_active.lib.db.predictions import FrameClassification, Prediction from encord_active.lib.project import Project # Setup device device = "cuda" if torch.cuda.is_available() else "cpu" print("device: ", device) # load clip model model, preprocess = clip.load("ViT-B/32", device=device) # Import encord project project_path = r'EAemotions' project = Project(Path(project_path)).load() In the code above, you loaded the CLIP “ViT-B/32” model, and the last three lines show how you can import an Encord project into the script using the Python SDK library. Next, load the project ontology and define the classes in the dataset. project_ontology = json.loads( (project.file_structure.project_dir/'ontology_output.json').read_text() ) ontology = json.loads( project.file_structure.ontology.read_text(encoding="utf-8") ) # Image classes classes = [] for option in ontology["classifications"][0]["attributes"][0]["options"]: classes.append(option["value"]) Since CLIP requires both images and encoded text to make classifications, create a function that makes texts out of the classes and encodes them. # encode class texts def generate_encoded_texts_from_classes(): tkns = [f'A photo of a {class_} face' for class_ in classes] texts = clip.tokenize(tkns).to(device) return texts encoded_texts = generate_encoded_texts_from_classes() Generate your custom metrics for this classification for performance comparison using the encord-active evaluation. Create a function that gets the label of each data_hash in the project. # Function to extract image label from label_rows metadata def get_label(classification_answers): k = list(classification_answers.keys())[0] classification_answers = classification_answers[k] answers = classification_answers['classifications'][0]['answers'] label = answers[0]['value'] return label Next, define variables to store “prediction labels,” “predictions to export,” “image paths,” and “image labels.”  The “prediction labels” consist of all the predicted classes;  “predictions to export” contains all the prediction objects;  “image paths” is a list of all the image paths of each data_hash;  “image labels” contain the true labels of each data_hash. # Variables my_predictions = []  # To store predicted labels predictions_to_import = []  # Predictions to be imported into encord-active image_paths = []  # List of all image paths # List of all image True classes image_labels = [     get_label(lr['classification_answers'])     for lr in project.label_rows.values() ] Note: These variables were created to make it easy for you to access their content for later use. Now, let’s make predictions. # Make predictions for item in tqdm(project.file_structure.iter_labels()):     for data_unit_hash, image_path in item.iter_data_unit():         data_unit_hash, image_path = str(data_unit_hash), str(image_path)         image_paths.append(image_path)         image = cv2.imread(image_path)         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)         image_transformed = transform_f(image)         with torch.no_grad():             logits_per_image, logits_per_text = model(                 image_transformed.to(device).unsqueeze(dim=0),                 encoded_texts             )             class_id = logits_per_image.argmax(dim=1, keepdim=True)[0][0].item()             model_prediction = project_ontology['classifications'][classes[class_id]]             my_predictions.append(classes[class_id])             confidence = logits_per_image.softmax(1).tolist()[0][class_id]             predictions_to_import.append(                 Prediction(                     data_hash=data_unit_hash,                     confidence=confidence,                     classification=FrameClassification(                         feature_hash=model_prediction['feature_hash'],                         attribute_hash=model_prediction['attribute_hash'],                         option_hash=model_prediction['option_hash'],                     ),                 )             ) In the code above, you looped through each data hash in the project label_rows metadata. From the metadata, you extracted the image path and image label. You read the image using the OpenCV library and applied some transformations.  It sends the transformed image and the encoded text list as input to the CLIP model. Then the prediction result was appended to the predictions_to_import list as a Prediction object. Now that you have stored all the CLIP predictions in a list (prediction_to_import) save them as a pickle file. # Export predictions with open(f"{project_path}/predictions.pkl", "wb") as f: pickle.dump(predictions_to_import, f) Next, generate your metrics so that you can compare them with Encord Active evaluations: # Metrics print(classification_report(     image_labels,     my_predictions, target_names=classes ) ) report = classification_report( image_labels, my_predictions, target_names=classes, output_dict=True ) mean_f1_score = report['macro avg']['f1-score'] mean_recall = report['macro avg']['recall'] mean_precision = report['macro avg']['precision'] print("Mean F1-score: ", mean_f1_score) print("Mean recall: ", mean_recall) print("Mean precision: ", mean_precision) cm = confusion_matrix(image_labels, my_predictions,) fig, ax = plt.subplots() im = ax.imshow(cm, cmap='Blues') cbar = ax.figure.colorbar(im, ax=ax) ax.set(xticks=np.arange(cm.shape[1]),   yticks=np.arange(cm.shape[0]),   xticklabels=classes,   yticklabels=classes,   xlabel='Predicted label',   ylabel='True label') plt.setp(ax.get_xticklabels(), rotation=45, ha="right",     rotation_mode="anchor") for i in range(cm.shape[0]): for j in range(cm.shape[1]):     ax.text(j, i, format(cm[i, j], 'd'),             ha="center", va="center",             color="white" if cm[i, j] > cm.max() / 2. else "black") ax.set_title("Confusion matrix") plt.show() Later in this article, you will use the predictions from the CLIP model as ground truth labels for training a CNN model. A function creates a new dataset using the CLIP predictions as ground truth labels. Execute the Python script using the command: python make_clip_predictions.py That should take a few minutes to execute. You can locate the Python script in the root directory. The script saves predictions.pkl in the Encord project directory and creates a new dataset with the CLIP predictions as GT labels.  Also, in your console output, you should see the metrics you coded: Import CLIP prediction into Encord Active In the previous code, you saved the model’s prediction in a pickle file. Import the model predictions into Active for model evaluation and run the following command from your root directory: # Change to Project directory cd ./EAemotions # Import Predictions encord-active import predictions.pkl # Start encord-active web app server encord-active visualize The commands above import the predictions into Encord and start a webserver on localhost - http://localhost:8501. Open the web server link in your browser. You should have something like the one below: Navigate to the project page by clicking “EAemotions” project. The project page should look like the one below: Click “Model Quality" in the left to view model evaluation options and metrics. A dropdown menu should appear. Select Metrics to see Encord Active's evaluation of the CLIP model. Interpreting the CLIP prediction results The classification metrics indicate poor performance by the CLIP model. An accuracy of 0.37 suggests that only 37% of the predictions are correct on the entire dataset. A mean precision of 0.42 indicates that, on average, the model is valid for 42% of the positive predictions. In contrast, a mean recall of 0.35 suggests it captures only 35% of the instances belonging to a class. The mean F1 score of 0.33 reflects the overall balance between precision and recall, but it is still relatively low.  Improving the model's performance may require addressing issues such as imbalanced data, model complexity, and feature representations through data augmentation, adjusting class weights, and using more sophisticated models. Metric importance quantifies the strength of the relationship between a metric (sharpness, brightness of an image, blurriness, e.t.c.) and model performance. A high importance value indicates that changes in the metric significantly impact the model's performance. For instance, altering this metric would strongly affect the model's performance if brightness is critical.  The values of this metric lie between 0 (no significant impact) and 1 (perfect correlation, where the metric alone can predict model performance). The visual above shows that image singularity, contrast, green values, sharpness, and blur have relatively little impact on the model’s performance. While the other metrics, such as blue values, area, brightness, etc., have no significance to the model’s performance. On the other hand, “Metric Correlations” assess the linearity and direction of the relationship between a metric and model performance. It informs whether a positive change in the metric leads to a positive change (positive correlation) or a negative change (negative correlation) in the model's performance. The correlation values range from -1 to 1, indicating the strength and direction of the relationship between the metric and the model's performance. Furthermore, you can filter the results by labels to see individual scores and model performance in a particular class. For example, the image below shows the scores and metrics for the “angry” class. Apply filters for other classes to gain more insight into the model's performance and how to improve it. In the next article, you will discover how to:  train a CNN model using the dataset created using labels that the CLIP model predicted. import the predictions into Encord for performance evaluation. Check out the project on Encord Active on GitHub and leave a star 🌟 if you like it, or an issue if you find something is missing—we love feedback! 🔥 NEW RELEASE: We released TTI-Eval (text-to-image evaluation), an open-source library for evaluating zero-shot classification models like CLIP and domain-specific ones like BioCLIP against your (or HF) datasets to estimate how well the model will perform. Get started with it on GitHub, and do ⭐️ the repo if it's awesome. 🔥

sampleImage_demystifying-deep-learning

Demystifying Deep Learning: What is Deep Learning?

You have likely heard of deep learning, but what is it actually?  Whether you are a seasoned data scientist, aspiring AI enthusiast or simply curious about the engine behind many modern technologies, this guide will demystify deep learning, providing an overview of its core concepts, mechanisms, and applications.  What is Deep Learning? Deep learning is a specialized branch of machine learning, a field nested within the broader realm of artificial intelligence.  Deep learning is termed "deep" due to its intricate neural networks architecture, a fundamental building block mirroring human brain’s complexity. These neural networks, termed artificial neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes, or artificial neurons, arranged in layers to collaboratively process data.  This elaborate arrangement empowers networks to independently unveil intricate patterns, capturing intricate data relationships—akin to how our brains decipher complex information. A basic neural network comprises three types of layers: the input layer, one or more hidden layers, and the output layer. Information flows from the input layer through the hidden layers to produce the final output. Each connection between neurons is associated with a weight, which the network adjusts during training to learn patterns in the training data. What distinguishes deep learning from traditional neural networks is the presence of multiple hidden layers. These deep architectures allow the network to automatically learn complex features and hierarchies in the data, enabling it to represent intricate relationships that were previously challenging for traditional machine learning models to capture. The Role of Neural Networks in Deep Learning To grasp the essence of deep learning, understanding the concept of neural networks is crucial. Artificial neurons are the building blocks of the neural networks. These neurons are mathematical functions that process input data and produce an output. Each neuron takes in weighted inputs, applies an activation function to compute a result, and passes it to the next layer. Activation functions introduce non-linearity, enabling neural networks to model highly complex relationships in data. Artificial neurons can be seen as simplified abstractions of biological neurons. Like their biological counterparts, they receive input signals, process them, and produce an output signal. The aggregation of these outputs across multiple neurons forms the network's prediction or classification. In order to understand the fundamental concepts of deep learning, learning the training process is important. Involving crucial methods like backpropagation and optimisation, this stage gives us the collective knowledge we need to observe how neural networks transform raw data into effective predicting engines. Training Neural Networks: Backpropagation and Optimization Training a neural network involves adjusting its weights to minimize the difference between predicted outputs and actual targets. This process is often referred to as optimization. One of the most crucial algorithms in deep learning is backpropagation, which drives the optimization process. Backpropagation works by calculating the gradient of the network's error with respect to its weights. This gradient indicates the direction in which the weights should be adjusted to minimize the error. Gradient descent algorithms use this information to iteratively update the weights, bringing the network's predictions closer to the desired outcomes. Deep learning frameworks provide a wide array of optimization algorithms, including stochastic gradient descent (SGD), Adam, and RMSProp, which influence how quickly the network converges to an optimal solution. The choice of optimization algorithm, along with other hyperparameters such as learning rate and batch size, significantly affects the training process's efficiency and effectiveness. Popular Neural Network Architectures After understanding the intricate details of backpropagation and optimisation for neural network training, our focus naturally moves on to analysing well-known neural network architectures. These architectures, born from the refined learning process, exemplify the art of optimization. We explore their complexity and show how different network configurations enhance their predictive power, demonstrating the underlying flexibility and power of their design. Convolutional Neural Networks (CNNs) for Image Analysis One of the most influential developments within deep learning is the rise of Convolutional Neural Networks (CNNs), a specialized architecture tailored for computer vision tasks. CNNs leverage the spatial relationships present in images by applying convolutional operations, which involve sliding small filters over the image's pixels to extract features. CNNs consist of alternating convolutional and pooling layers, followed by fully connected layers for classification. Convolutional layers extract hierarchical features from images, while pooling layers reduce the spatial dimensions of the data, enhancing computational efficiency and reducing the risk of overfitting. Understanding Convolutional Neural Networks (CNNs): A Complete Guide Recurrent Neural Networks (RNNs) for Sequential Data While CNNs excel in tasks involving spatial data, Recurrent Neural Networks (RNNs) are designed to handle sequential data, where the order of elements matters. This makes RNNs ideal for tasks like natural language processing, speech recognition, and time series analysis. RNNs maintain a hidden state that captures information about previous inputs in the sequence. This hidden state is updated with each new input, allowing the network to learn dependencies and patterns over time. However, traditional RNNs often struggle to capture long-range dependencies due to the vanishing gradient problem, where gradients diminish as they are backpropagated through time. Recurrent Neural Network(RNN) Tutorial: Types, Examples, LSTM and More Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) To address the challenges posed by the vanishing gradient problem, researchers introduced specialized RNN variants known as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs). These architectures incorporate gated mechanisms that regulate the flow of information within the network's hidden states, enabling them to capture long-term dependencies more effectively. Long short-term memory LSTMs and GRUs consist of gates that control the input, output, and update of information in the hidden state. These gates, driven by sigmoid and tanh activation functions, determine which information to retain, forget, or output. This mechanism has significantly improved the performance of RNNs in various sequence-related tasks. Generative Adversarial Networks (GANs) Deep learning isn't confined to supervised and unsupervised learning paradigms alone. Generative Adversarial Networks (GANs) represent an innovative approach to generative modeling. GANs consist of two neural networks: a generator and a discriminator, pitted against each other in a competitive setting. The generator's objective is to produce data that is indistinguishable from real data, while the discriminator's goal is to differentiate between real and generated data. Through this adversarial process, the generator becomes increasingly adept at creating convincing data, leading to the generation of realistic images, videos, music, and even text. Generative Adversarial Networks GANs have found applications in various creative domains, including art generation, style transfer, and content creation. They have also raised ethical concerns related to the generation of deepfake content and the potential for misuse. Transfer Learning and Pretrained Models Training deep neural networks from scratch often requires substantial computational resources and time. Transfer learning offers a solution by leveraging pretrained models. In transfer learning, a model trained on a large dataset for a specific task is fine-tuned for a related task with a smaller dataset. Transfer learning significantly accelerates the training process and improves performance, as the initial model has already learned a wide range of features. Popular pretrained models, such as BERT for natural language processing and ImageNet-trained CNNs for image analysis, have become valuable assets in the deep learning toolkit. Applications of Real-World Deep Learning Deep learning's impact is evident across various domains, transforming industries and enhancing capabilities. Some notable applications include: Healthcare: Deep learning has revolutionized medical imaging, enabling accurate diagnoses from X-rays, MRIs, and CT scans. It aids in disease detection, such as identifying diabetic retinopathy from retinal images and detecting early signs of cancer. Autonomous Vehicles: Deep learning is at the heart of self-driving cars, enabling them to perceive and understand the surrounding environment through sensor data. It plays a crucial role in object detection, lane tracking, and decision-making. Natural Language Processing (NLP): Deep learning has fueled advancements in NLP, enabling machines to understand, generate, and translate human language. Chatbots, language translation, sentiment analysis, and content recommendation systems are just a few examples. Finance: In the financial sector, deep learning algorithms analyze market data to predict stock prices, detect fraudulent transactions, and manage investment portfolios more effectively. Entertainment: Deep learning enhances the entertainment industry by enabling content recommendation on streaming platforms, improving video game AI, and even generating music and art. Future Prospects and Challenges As deep learning continues to evolve, researchers and practitioners are exploring avenues for improvement and addressing challenges: Interpretability: Understanding why deep learning models make specific decisions remains a challenge. Interpretable models are crucial, especially in critical applications like healthcare, where decisions must be explainable to medical professionals and patients. Data Efficiency: Deep learning models typically require large amounts of data for training. Research into techniques that can make deep learning more data-efficient is ongoing, as collecting labeled data can be expensive and time-consuming. Ethical Considerations: The rise of GANs has raised concerns about the potential misuse of generated content, leading to the spread of misinformation and deepfake videos. Ethical guidelines and regulations are necessary to ensure responsible use. Robustness and Security: Deep learning models are vulnerable to adversarial attacks, where small, imperceptible changes to input data can lead to incorrect predictions. Developing robust and secure models is crucial for applications in sensitive domains. Deep Learning: Key Takeaways Complex Pattern Recognition: Deep learning employs intricate neural networks to automatically decipher complex patterns in data, enabling machines to excel at tasks like image recognition, language translation, and even creativity. Hierarchy of Features: Unlike traditional methods, deep learning's multiple hidden layers enable it to learn hierarchical features, capturing intricate relationships in data that were previously challenging to represent. Diverse Applications: Deep learning's impact spans various sectors, including healthcare, autonomous vehicles, finance, and entertainment. It's revolutionizing how we diagnose diseases, navigate self-driving cars, and even generate art. Continuous Evolution: As the field evolves, challenges like interpretability, data efficiency, and ethical considerations need addressing. Deep learning's potential is immense, but responsible development is essential to harness its power effectively.

Aug 21 2023

sampleImage_time-series-predictions-with-recurrent-neural-networks

Time Series Predictions with RNNs

Time series prediction, or time series forecasting, is a branch of data analysis and predictive modeling that aims to make predictions about future values based on historical data points in chronological order. In a time series, data is collected and recorded over regular intervals of time (i.e. hourly, daily, monthly, or yearly). Examples of time series data include stock prices, weather measurements, sales figures, website traffic, and more.  Recurrent Neural Networks (RNNs) are deep learning models that can be utilized for time series analysis, with recurrent connections that allow them to retain information from previous time steps. Popular variants include Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which can learn long-term dependencies. In this article, you will learn about:  Time Series Data Recurrent Neural Networks (RNNs) Building and training the Recurrent Neural Networks (RNN) Model Evaluating the Model Performance Limitations of Time Series Predictions with Recurrent Neural Networks (RNNs) Advanced Techniques for Time Series Predictions Applying Recurrent Neural Networks (RNNs) on real data (Using Python and Keras) To learn about activation functions, read: Activation Functions in Neural Networks: With 15 examples. What is Time Series Data? Time series data is a sequence of observations recorded over time, where each data point is associated with a specific timestamp. This data type is widely used in various fields to analyze trends, make predictions, and understand temporal patterns. Time series data has unique characteristics, such as temporal ordering, autocorrelation (where a data point depends on its previous ones), and seasonality (repeating patterns over fixed intervals). Recurrent Neural Networks (RNNs) bring a unique edge to time series forecasting, empowering you to capture intricate temporal dependencies. Exploring the realm of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models unveils the genuine potential of predictive analytics.   Types of Time Series Patterns Time series data analysis involves identifying various patterns that provide insights into the underlying dynamics of the data over time. These patterns shed light on the trends, fluctuations, and noise present in the dataset, enabling you to make informed decisions and predictions. Let's explore some of the prominent time series patterns that help us decipher the intricate relationships within the data and leverage them for predictive analytics.  From discerning trends and seasonality to identifying cyclic patterns and understanding the impact of noise, each pattern contributes to our understanding of the data's behavior over time. Additionally, time series regression introduces a predictive dimension, allowing you to forecast numerical values based on historical data and the influence of other variables. Delving into the below patterns not only offers a world of insights within time-dependent data but also unearths distinct components that shape its narrative: Trends: Trends represent long-term changes or movements in the data over time. These can be upward (increasing trend) or downward (decreasing trend), indicating the overall direction in which the data is moving. Seasonality: Seasonality refers to repeating patterns or fluctuations that occur at regular intervals. These patterns might be daily, weekly, monthly, or yearly, depending on the nature of the data. Cyclic Patterns: Unlike seasonality, cyclic patterns are not fixed to specific intervals and may not repeat at regular frequencies. They represent oscillations that are not tied to a particular season. Noise: Noise is the random variation present in the data which does not follow any specific pattern. It introduces randomness and uncertainty to the time series. Regression: Time series regression involves building a predictive model to forecast a continuous numerical value (the dependent variable) based on historical time series data of one or more predictors (independent variables).  Preprocessing Techniques for Time Series Data Before applying any prediction model, proper preprocessing is essential for time series data. Some common preprocessing techniques include: Handling Missing Values: Addressing missing values is crucial as gaps in the data can affect the model's performance. You can use techniques like interpolation or forward/backward filling. Data Normalization: Normalizing the data ensures that all features are on the same scale, preventing any single feature from dominating the model's learning process. Detrending: Removing the trend component from the data can help in better understanding the underlying patterns and making accurate predictions. Seasonal Adjustment: For data with seasonality, seasonal adjustment methods like seasonal differencing or seasonal decomposition can be applied. Smoothing: Smoothing techniques like moving averages can be used to reduce noise and highlight underlying patterns. Train-test Split: It is crucial to split the data into training and test sets while ensuring that the temporal order is maintained. This allows the model to learn from past input data of the training set and evaluate its performance on unseen future data. Mastering the train-validation-test split is crucial for robust machine learning models. Learn how to segment datasets effectively, prevent overfitting, and optimize model performance in our comprehensive guide on Training, Validation, and Test Split for Machine Learning Datasets. With a grasp of the characteristics of time series data and the application of suitable preprocessing methods, you can lay the groundwork for constructing resilient predictive models utilizing Recurrent Neural Networks (RNNs). Recurrent Neural Networks (RNNs) A Recurrent Neural Network (RNN) is like a specialized brain for handling sequences, such as sentences or time-based data. Imagine it as a smart cell with its own memory. For example, think about predicting words in a sentence. The RNN not only understands each word but also remembers what came before using its internal memory. This memory helps it capture patterns and relationships in sequences. This makes RNNs great for tasks like predicting future values in time series data, like stock prices or weather conditions, where past information plays a vital role. Advantages Recurrent Neural Networks (RNNs) offer several advantages for time series prediction tasks. They can handle sequential data of varying lengths, capturing long-term dependencies and temporal patterns effectively. RNNs accommodate irregularly spaced time intervals and adapt to different forecasting tasks with input and output sequences of varying lengths. However, RNNs have limitations like the vanishing or exploding gradient problem, which affects their ability to capture long-term dependencies because RNNs may be unrolled very far back in this Memory constraints may also limit their performance with very long sequences. While techniques like LSTMs and GRUs mitigate some issues, other advanced architectures like Transformers might outperform RNNs in certain complex time series scenarios, necessitating careful model selection. Limitations The vanishing gradient problem is a challenge that affects the training of deep neural networks, including Recurrent Neural Networks (RNNs). It occurs when gradients, which indicate the direction and magnitude of updates to network weights during training, become very small as they propagate backward through layers. This phenomenon hinders the ability of RNNs to learn long-range dependencies and can lead to slow or ineffective training.  The vanishing gradient problem is particularly problematic in sequences where information needs to be remembered or propagated over a long span of time, affecting the network's ability to capture important patterns. To combat the vanishing gradient problem that hampers effective training in neural networks, several strategies have emerged. Techniques like proper weight initialization, batch normalization, gradient clipping, skip connections, and learning rate scheduling play pivotal roles in stabilizing gradient flow and preventing their untimely demise. Amid this spectrum of approaches, two standout solutions take center stage: LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit). Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Traditional RNNs struggle with the vanishing gradient problem, which makes it difficult for the network to identify long-term dependencies in sequential data. However, this challenge is elegantly addressed by LSTM, as it incorporates specialized memory cells and gating mechanisms that preserve and control the flow of gradients over extended sequences. This enables the network to capture long-term dependencies more effectively and significantly enhances its ability to learn from sequential data. LSTM has three gates (input, forget, and output) and excels at capturing long-term dependencies. Gated Recurrent Unit (GRU), a simplified version of LSTM with two gates (reset and update), maintains efficiency and performance similar to LSTM, making it widely used in time series tasks. In 1997, Sepp Hochreiter and Jürgen Schmidhuber published a seminal paper titled "Long Short-Term Memory" that introduced the LSTM network. Both the scientists are pioneers in the field of Artificial Intelligence and Machine Learning.   Building and Training the Recurrent Neural Networks (RNNs) Model for Time Series Predictions Building and training an effective RNN model for time series predictions requires an approach that balances model architecture and training techniques. This section explores all the essential steps for building and training an RNN model. The process includes data preparation, defining the model architecture, building the model, fine-tuning hyperparameters, and then evaluating the model’s performance. Data Preparation Data preparation is crucial for accurate time series predictions with RNNs. Handling missing values and outliers, scaling data, and creating appropriate input-output pairs are essential. Seasonality and trend removal help uncover patterns, while selecting the right sequence length balances short- and long-term dependencies.  Feature engineering, like lag features, improves model performance. Proper data preprocessing ensures RNNs learn meaningful patterns and make accurate forecasts on unseen data. Building the RNN Model Building the RNN model includes a series of pivotal steps that collectively contribute to the model’s performance and accuracy. Designing the RNN Architecture: Constructing the RNN architecture involves deciding the layers and the neurons in the network. A typical structure for time series prediction comprises an input layer, one or more hidden layers with LSTM or GRU cells and an output layer. Selecting the optimal number of layers and neurons: This is a critical step in building the RNN model. Too few layers or neurons may lead to underfitting, while too many can lead to overfitting. It's essential to strike a balance between model complexity and generalization. You can use techniques like cross-validation and grid search to find the optimal hyperparameters. Hyperparameter tuning and optimization techniques: Hyperparameter tuning involves finding the best set of hyperparameters for the RNN model. Hyperparameters include learning rate, batch size, number of epochs, and regularization strength. You can employ grid or randomized search to explore different combinations of hyperparameters and identify the configuration that yields the best performance. Hyperparameter Tuning Training the Recurrent Neural Networks (RNNs) Model Training an RNN involves presenting sequential data with learning algorithms to the model and updating its parameters iteratively to minimize the prediction error.  By feeding historical sequences into the RNN, it learns to capture patterns and dependencies in the data. The process usually involves forward propagation to compute predictions and backward propagation to update the model's weights using optimization algorithms like Stochastic Gradient Descent (SGD) or Adam. Stochastic Gradient Descent, Learning Rate = 0.01 Backpropagation through time (BPTT) is a variant of the standard backpropagation algorithm used in RNNs.  Backpropagation through time (BPTT) Overfitting is a common issue in deep learning models, including RNNs. You can employ regularization techniques like L1 and L2 regularization, dropout, and early stopping to prevent overfitting and improve the model's generalization performance. Evaluating Model Performance To assess the performance of the trained RNN model, you can use evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). These metrics quantify the accuracy of the predictions compared to the actual values and provide valuable insights into the model's effectiveness. Visualizing the model's predictions against the actual time series data can help you understand its strengths and weaknesses. Plotting the predicted values alongside the true values provides an intuitive way to identify patterns, trends, and discrepancies. Interpreting the results involves analyzing the evaluation metrics, visualizations, and any patterns or trends observed.  Based on the analysis, you can identify potential improvements to the model. These may include further tuning hyperparameters, adjusting the architecture, or exploring different preprocessing techniques. By carefully building, training, and evaluating the RNN model, you  can develop a powerful tool for time series prediction that can capture temporal dependencies and make accurate forecasts. Limitations of Time Series Predictions with Recurrent Neural Networks (RNNs) While Recurrent Neural Networks (RNNs) offer powerful tools for time series predictions, they have certain limitations. Understanding these limitations is crucial for developing accurate and reliable predictive models. RNNs may struggle with capturing long-term dependencies, leading to potential prediction inaccuracies.  Additionally, training deep RNNs can be computationally intensive, posing challenges for real-time applications. Addressing these limitations through advanced architectures and techniques is essential to harnessing the full potential of RNNs in time series forecasting. Non-Stationary Time Series Data Non-stationary time series data exhibits changing statistical properties such as varying mean or variance, over time. Dealing with non-stationarity is crucial, as traditional models assume stationarity.  Techniques like differencing, detrending, or seasonal decomposition can help transform the data into a stationary form. Additionally, advanced methods like Seasonal Autoregressive Integrated Moving Average (SARIMA) or Prophet can be used to model and forecast non-stationary time series. Data with Irregular Frequencies and Missing Timestamps Real-world time series data can have irregular frequencies and missing timestamps, disrupting the model's ability to learn patterns. You can apply resampling techniques (e.g., interpolation, aggregation) to convert data to a regular frequency. For missing timestamps, apply imputation techniques like forward and backward filling or more advanced methods like time series imputation models. Time series data: a single pixel produces an irregular series of raw events Concept Drift and Model Adaptation In dynamic environments, time series data might undergo concept drift, where the underlying patterns and relationships change over time. To address this, the model needs to adapt continuously. Use techniques like online learning and concept drift detection algorithms to monitor data distribution changes and trigger model updates when necessary. Advanced Techniques and Improvements As time series data becomes more complex and diverse, advanced techniques are essential to enhance the capabilities of Recurrent Neural Networks (RNNs). Multi-variate time series data featuring multiple interconnected variables can be effectively handled by extending RNNs to accommodate multiple input features and output predictions. Incorporating attention mechanisms refines RNN predictions by prioritizing relevant time steps or features, especially in longer sequences.  Also, combining RNNs with other models like CNN-RNN, Transformer-RNN, or ANN-RNN makes hybrid architectures that can handle both spatial and sequential patterns. This improves the accuracy of predictions in many different domains. These sophisticated techniques empower RNNs to tackle intricate challenges and deliver comprehensive insights. Multi-Variate Time Series Data in Recurrent Neural Networks (RNNs) In many real-world scenarios, time series data may involve multiple related variables. You can extend RNNs to handle multi-variate time series by incorporating multiple input features and predicting multiple output variables. This allows the model to leverage additional information to make more accurate predictions and better capture complex relationships among different variables. Multivariate Time Series Attention Mechanisms for More Accurate Predictions Attention mechanisms enhance RNNs by focusing on relevant time steps or features during predictions. They improve accuracy and interpretability, especially in long sequences. Combining RNNs with other models, like the convolutional neural network model CNN-RNN or Transformer-RNN, Artificial Neural Networks ANN-RNN, may further boost performance for time series tasks.  ANNs consist of interconnected artificial neurons, nodes or units, organized into layers. Hybrid models effectively handle spatial and sequential patterns, leading to better domain predictions and insights. Advanced techniques like Seq-2-Seq, bidirectional, transformers etc. make RNNs more adaptable, addressing real-world challenges and yielding comprehensive results.  Case Study: Applying Recurrent Neural Networks (RNNs) to Real Data This case study uses Recurrent Neural Networks (RNNs) to predict electricity consumption based on historical data. The "Electricity Consumption'' dataset contains hourly electricity consumption data over a period of time. The aim is to build an RNN model to forecast future electricity consumption, leveraging past consumption patterns. Python Code (using Pytorch): import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler import torch import torch.nn as nn from torch.utils.data import DataLoader, Dataset from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, mean_squared_error # Sample data for Electricity Consumption  data = {     'timestamp': pd.date_range(start='2023-01-01', periods=100, freq='H'),     'consumption': np.random.randint(100, 1000, 100) } df = pd.DataFrame(data) df.set_index('timestamp', inplace=True) # Preprocessing scaler = MinMaxScaler() df_scaled = scaler.fit_transform(df) # Create sequences and labels for training seq_length = 24 X, y = [], [] for i in range(len(df_scaled) - seq_length): X.append(df_scaled[i:i + seq_length]) y.append(df_scaled[i + seq_length]) X, y = np.array(X), np.array(y) # Split the data into training and test sets train_size = int(0.8 * len(X)) X_train, X_test = X[:train_size], X[train_size:] y_train, y_test = y[:train_size], y[train_size:] # Create a custom dataset class for PyTorch DataLoader class TimeSeriesDataset(Dataset): def __init__(self, X, y): self.X = torch.tensor(X, dtype=torch.float32) self.y = torch.tensor(y, dtype=torch.float32) def __len__(self): return len(self.X) def __getitem__(self, index): return self.X[index], self.y[index] # Define the RNN model class RNNModel(nn.Module): def __init__(self, input_size, hidden_size, output_size): super(RNNModel, self).__init__() self.rnn = nn.LSTM(input_size, hidden_size, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x): out, _ = self.rnn(x) out = self.fc(out[:, -1, :]) return out # Hyperparameters input_size = X_train.shape[2] hidden_size = 128 output_size = 1 learning_rate = 0.001 num_epochs = 50 batch_size = 64 # Create data loaders train_dataset = TimeSeriesDataset(X_train, y_train) train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) # Initialize the model, loss function, and optimizer model = RNNModel(input_size, hidden_size, output_size) criterion = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) # Training the model for epoch in range(num_epochs): for inputs, targets in train_loader: outputs = model(inputs) loss = criterion(outputs, targets) optimizer.zero_grad() loss.backward() optimizer.step() if (epoch + 1) % 10 == 0: print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}') # Evaluation on the test set model.eval() with torch.no_grad(): X_test_tensor = torch.tensor(X_test, dtype=torch.float32) y_pred = model(X_test_tensor).numpy() y_pred = scaler.inverse_transform(y_pred) y_test = scaler.inverse_transform(y_test) # Calculate RMSE mse = mean_squared_error(y_test, y_pred) rmse = np.sqrt(mse) print(f"Root Mean Squared Error (RMSE): {rmse}") mae = mean_absolute_error(y_test, y_pred) print(f"Mean Absolute Error (MAE): {mae:.2f}") mape = mean_absolute_percentage_error(y_test, y_pred) * 100 print(f"Mean Absolute Percentage Error (MAPE): {mape:.2f}%") # Visualize predictions against actual data plt.figure(figsize=(10, 6)) plt.plot(df.index[train_size+seq_length:], y_test, label='Actual') plt.plot(df.index[train_size+seq_length:], y_pred, label='Predicted') plt.xlabel('Timestamp') plt.ylabel('Electricity Consumption') plt.title('Electricity Consumption Prediction using RNN (PyTorch)') plt.legend() plt.show() Time Series with Recurrent Neural Networks RNN - Github The provided code demonstrates the implementation of a Recurrent Neural Network (RNN) using PyTorch for electricity consumption prediction. The model is trained and evaluated on a sample dataset. The training process includes 50 epochs, and the loss decreases over iterations, indicating the learning process.  The analysis reveals significant gaps between predicted and actual electricity consumption values, as indicated by the relatively high Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). These metrics collectively suggest that the current model's predictive accuracy requires improvement. The deviations underscore that the model falls short in capturing the true consumption patterns accurately. In the context of the case study, where the goal is to predict electricity consumption using Recurrent Neural Networks (RNNs), these results highlight the need for further fine-tuning.  Despite leveraging historical consumption data and the power of RNNs, the model's performance indicates a discrepancy between predicted and actual values. The implication is that additional adjustments to the model architecture, hyperparameters, or preprocessing of the dataset are crucial. Enhancing these aspects could yield more reliable predictions, ultimately leading to a more effective tool for forecasting future electricity consumption patterns. "RNNs have revolutionized time series analysis, enabling us to predict future values with remarkable accuracy. Through the lens of LSTM and GRU, you can decipher hidden patterns within temporal data, paving the way for transformative insights in diverse industries." - Dr. John Smith   Time Series Predictions with Recurrent Neural Networks (RNNs): Key Takeaways Time series data possesses unique characteristics, necessitating specialized techniques for analysis and forecasting. Recurrent Neural Networks (RNNs) excel in handling sequences, capturing dependencies, and adapting to diverse tasks. Proper data preparation, model building, and hyperparameter tuning are crucial for successful RNN implementation. Evaluation metrics and visualization aid in assessing model performance and guiding improvements. Addressing real-world challenges requires advanced techniques like attention mechanisms and hybrid models. Time Series Predictions with Recurrent Neural Networks (RNNs): Frequently Asked Questions What is time series data? Time series data is a sequence of observations recorded over time, often used in fields like finance and weather forecasting. Its uniqueness lies in temporal ordering, autocorrelation, seasonality, cyclic patterns, and noise, which necessitate specialized techniques for analysis and prediction. What are recurrent neural networks (RNNs) and their advantages? RNNs are specialized neural networks designed for sequential data analysis. They excel in handling varying sequence lengths, capturing long-term dependencies, and adapting to irregular time intervals. RNNs are proficient in tasks requiring an understanding of temporal relationships. How do LSTM and GRU models address challenges like the vanishing gradient problem? Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models are RNN variations that mitigate the vanishing gradient problem. They incorporate gating mechanisms that allow them to retain information from previous time steps, enabling the learning of long-term dependencies. What challenges do recurrent neural networks (RNNs) face, and how can they be overcome? While RNNs offer powerful capabilities, they also have limitations, including computational demands and potential struggles with very long sequences. Addressing these challenges requires meticulous hyperparameter tuning, careful data preparation, and techniques like regularization. How can recurrent neural networks (RNNs) be applied to real-world time series data? Applying RNNs to real-world time series data involves a comprehensive process. It begins with proper data preprocessing, designing the RNN architecture, tuning hyperparameters, and training the model. Evaluation metrics and visualization are used to assess performance and guide improvements, addressing challenges like non-stationarity, missing timestamps, and more.

Aug 18 2023

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Dual-Stream Diffusion Net for Text-to-Video Generation

Even in the rapidly advancing field of artificial intelligence, converting text to captivating video content remains a challenge. The introduction of Dual-Stream Diffusion Net (DSDN) represents a significant innovation in text-to-video generation, creating a solution that combines text and motion to create personalized and contextually rich videos. In this article, we will explore the intricacies of Hugging Face’s new Dual-Stream Diffusion Net, decoding its architecture, forward diffusion process, and dual-stream mechanism. By examining the motion decomposition and fusion process, we can understand how DSDN generates realistic videos. With empirical evidence from experiments, we establish DSDN's superiority. DSDN’s implications reach beyond technology; it also marks a step forward in the future of content creation and human-AI collaboration. Far from being simply a video-generation tool, DSDN contributes to crafting experiences that resonate with audiences, revolutionizing entertainment, advertising, education, and more. Text-to-Video Generation Transforming textual descriptions into visual content is both an exciting and challenging task. This endeavor not only advances the field of natural language processing but also unlocks extensive possibilities for various applications like entertainment, advertising, education, and surveillance. While text-to-image generation has been well researched, AI text-to-video generation introduces an additional layer of complexity. Videos carry not only spatial content but also dynamic motion, making the generation process more intricate. Although recent progress in diffusion models have offered promising solutions, challenges such as flickers and artifacts in generative in generated videos remain a bottleneck. Dual-Stream Diffusion Net: Architecture To address the limitations of existing methods, Hugging Face has proposed a novel approach called the Dual-Stream Diffusion Net (DSDN). Specifically engineered to improve consistency in content variations within generated videos, DSDN integrates two independent diffusion streams: a video content branch and a motion branch. These streams operate independently to generate personalized video variations while also being aligned to ensure smooth and coherent transitions between content and motion. Dual-Stream Diffusion Net for Text-to-Video Generation Forward Diffusion Process The foundation of DSDN lies in the Forward Diffusion Process (FDP), a key concept inspired by the Denoising Diffusion Probabilistic Model (DDPM). In the FDP, latent features undergo a noise perturbation through a Markov process. The shared noising schedule between the two streams ensures that the content and motion branches progress in harmony during the diffusion process. This prepares the priors necessary for the subsequent denoising steps. Personalized Content Generation Stream The content generation process leverages a pre-trained text-to-image conditional diffusion model and an incremental learning module introduced by DSDN. The model dynamically refines content generation through a content basic unit and a content increment unit. This combination not only maintains the content's quality but also ensures that personalized variations align with the provided text prompts. Personalized Motion Generation Stream Motion is addressed through a Personalized Motion Generation Stream. The process utilizes a 3D U-Net based diffusion model to generate motion-coherent latent features. These features are generated alongside content features and are conditioned on both content and textual prompts. This method ensures that the generated motion is aligned with the intended content and context. Dual-Stream Transformation Interaction One of DSDN’s distinct features is the Dual-Stream Transformation Interaction module. By employing cross-transformers, this module establishes a connection between the content and motion streams. During the denoising process, information from one stream is integrated into the other, enhancing the overall continuity and coherence of the generated videos. This interaction ensures that content and motion are well-aligned, resulting in smoother and more realistic videos. Motion Decomposition and Combination DSDN introduces motion decomposition and combination techniques to manage motion information more effectively. The system employs a motion decomposer that extracts motion features from adjacent frames, capturing the inter-frame dynamics. These motion features are subsequently combined with content features using a motion combiner. This approach enhances the generation of dynamic motion while maintaining content quality. Dual-Stream Diffusion Net: Experiments The experimental evaluation of the Dual-Stream Diffusion Net (DSDN) highlights it promise in the field of text-to-video generation relative to comparable models such as CogVideo and Text2Video-Zero. DSDN emerges as a definitive frontrunner, surpassing established benchmarks with its remarkable performance and innovative approach. Dual-Stream Diffusion Net for Text-to-Video Generation DSDN's exceptional ability to maintain frame-to-frame consistency and text alignment makes it stand out. For the assessment, CLIP image embeddings are computed. Unlike its counterparts that exhibit discrepancies in contextual alignment, DSDN masters the art of integrating textual inputs flawlessly into the visual narrative. This unparalleled ability underscores DSDN's deep comprehension of linguistic subtleties, yielding videos that remain faithful to the essence of the input text. In terms of content quality and coherence, DSDN shows promising results. Where CogVideo and Text2Video-Zero may struggle with maintaining motion coherence and generating content that resonates with user preferences, DSDN excels. Its unique dual-stream architecture, combined with stable diffusion techniques, ensures that the generated videos possess both visual appeal and contextual accuracy. This dynamic fusion transforms synthetic content into captivating visual stories, a feat that other models struggle to achieve. Read the original paper published by Hugging Face, authored by Binhui Liu, Xin Liu, Anbo Dai, Zhiyong Zeng, Zhen Cui, and Jian Yang available on Arxiv: Dual-Stream Diffusion Net for Text-to-Video Generation.   Dual-Stream Diffusion Net: Key Takeaways The Dual-Stream Diffusion Net (DSDN) architecture combines personalized content and motion generation for context-rich video creation. DSDN's dual-stream approach enables simultaneous yet cohesive development of video content and motion, yielding more immersive and coherent videos. Through meticulous motion decomposition and recombination, DSDN achieves seamless integration of elemental motion components, enhancing visual appeal. DSDN explores the interaction between content and motion streams, iteratively refining their fusion to produce highly realistic and personalized videos. Empirical experiments demonstrate DSDN's superiority, surpassing existing methods in contextual alignment, motion coherence, and user preference in content generation, signifying its transformative potential in content generation.

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FastViT: Hybrid Vision Transformer with Structural Reparameterization

In the constantly evolving field of computer vision, recent advancements in machine learning have paved the way for remarkable growth and innovation.  A prominent development in this area has been the rise of Vision Transformers (ViTs), which have demonstrated significant capabilities in handling various vision tasks. These ViTs have begun to challenge the long-standing prominence of Convolutional Neural Networks (CNNs), thanks in part to the introduction of hybrid models that seamlessly combine the advantages of both ViTs and CNNs. This blog post explores the innovative FastViT model, a hybrid vision transformer that employs structural reparameterization. This approach leads to notable improvements in speed, efficiency, and proficiency in representation learning, marking an exciting development in the field. Vision Transformers Vision Transformers, initially introduced by Dosovitskiy et al. in the paper "An Image is Worth 16x16 Words" revolutionized computer vision by directly applying the transformer architecture to image data. Instead of relying on convolutional layers like traditional CNNs, ViTs process images as sequences of tokens, enabling them to capture global context efficiently. However, ViTs often demand substantial computational resources, limiting their real-time application potential. Hybrid Vision Transformers Hybrid models combine the best of both worlds – the strong feature extraction capabilities of CNNs and the attention mechanisms of transformers. This synergy leads to improved efficiency and performance. Hybrid Vision Transformers utilize the feature extraction capabilities of CNNs as their backbone and integrate this with the self-attention mechanism inherent in transformers. Structural Reparameterization FastViT introduces an innovative concept known as structural reparameterization. This technique optimizes the architecture's structural elements to enhance efficiency and runtime. By carefully restructuring the model, FastViT reduces memory access costs, resulting in significant speed improvements, especially at higher resolutions. The reparameterization strategy aligns with the "less is more" philosophy, underscoring that a well-designed architecture can outperform complex counterparts. FastViT Architecture The FastViT architecture builds upon the hybrid concept and structural reparameterization. Instead of using the complex mesh regression layers typically seen in 3D hand mesh estimation models, it employs a more streamlined regression module. This module predicts weak perspective camera, pose, and shape parameters, demonstrating that powerful feature extraction backbones can alleviate the challenges in mesh regression. FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization The code for running and evaluating FastViT is available on Apple’s GitHub Repository.   FastViT Experiments In experiments, FastViT showcases speed enhancements, operating 3.5 times faster than CMT, a recent state-of-the-art hybrid transformer architecture. It also surpasses EfficientNet by 4.9 times and ConvNeXt by 1.9 times in speed on a mobile device, all the while maintaining consistent accuracy on the ImageNet dataset. Notably, when accounting for similar latency, FastViT achieves a 4.2% improvement in Top-1 accuracy on ImageNet when compared to MobileOne. These findings highlight the FastViT model's superior efficiency and performance relative to existing alternatives. FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization Image Classification FastViT is evaluated against the widely-used ImageNet-1K dataset. The models are trained for several epochs using the AdamW optimizer. The results highlight FastViT's ability to strike an impressive balance between accuracy and latency. It outperforms existing models on both desktop-grade GPUs and mobile devices, showcasing its efficiency and robustness. Robustness Evaluation Robustness is vital for practical applications. In this regard, FastViT stands out. It exhibits superior performance against rival models, especially in challenging scenarios where robustness and generalization are crucial. This emphasizes its proficiency in representation learning across diverse contexts. 3D Hand Mesh Estimation FastViT also performs well in 3D hand mesh estimation, a critical task in gesture recognition. Unlike other techniques that depend on complicated mesh regression layers, FastViT's structural reparameterization allows for a simpler regression module that yields superior results. This approach outperforms existing real-time methods, showcasing its accuracy and efficiency. FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization as Image Encoder. Semantic Segmentation & Object Detection The efficiency of FastViT is also evident in semantic segmentation and object detection tasks. Its performance on the ADE20k dataset and MS-COCO dataset demonstrates versatility and competitiveness in diverse computer vision applications. Read the original paper published by Apple, authored by Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, Anurag Ranjan available on Arxiv: FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization.   FastViT: Key Takeaways Efficient Hybrid Vision Transformer: FastViT combines the strengths of Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) to create an efficient hybrid architecture. Structural Reparameterization: FastViT introduces a groundbreaking concept known as structural reparameterization, which optimizes the model's architecture for enhanced efficiency and runtime. Memory Access Optimization: Through structural reparameterization, FastViT reduces memory access costs, resulting in significant speed improvements, especially for high-resolution images. Global Context and Efficiency: FastViT leverages the attention mechanisms of transformers to capture global context efficiently, making it an ideal candidate for a wide range of computer vision tasks.

Aug 17 2023

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Meta AI’s Photorealistic Unreal Graphics (PUG)

Meta AI’s FAIR team released the Photorealistic Unreal Graphics (PUG) dataset family, a significant innovation in the field of representation learning research. Consisting of three targeted datasets - PUG: Animal, PUG: ImageNet, and PUG: SPAR - this collection provides images poised to contribute to the ongoing evolution of artificial intelligence technologies. These datasets are a marriage of state-of-the-art simulation techniques and AI innovation. While these datasets are accessible as part of the Meta AI community's contributions, they come with specific licensing terms (CC-BY-NC) and are not meant for Generative AI uses, thereby maintaining their research-centric orientation. Sourced from the Unreal Engine Marketplace and Sketchfab, the images were manually compiled to ensure high quality. With PUG: Animals offering 215,040 images, PUG: ImageNet at 88,328, and PUG: SPAR at 43,560, the PUG dataset family stands as a versatile resource that underscores a marked advancement in artificial intelligence research. Photorealistic Synthetic Data In the field of machine learning, the need for extensive and relevant data is paramount. However, the focus is not solely on quantity: it's the quality and characteristics of the data that dictate a model's efficacy. Controllability and realism are central to understanding how models respond to different scenarios, ensuring robustness and adaptability in the real world. Photorealistic synthetic data has emerged as an effective solution that combines these attributes. By leveraging advanced simulation techniques, photorealistic synthetic data mirrors real-world scenarios with precision. Beyond simple imitation, photorealistic synthetic image datasets allow researchers to manipulate aspects such as lighting, textures and poses with precision. This fine-grained control facilitates comprehensive experimentation and model evaluation. In addition, photorealistic synthetic data addresses challenges related to the lack of real-world data, supplying ample training material to help models adapt and generalize. Working with synthetic data? Read about how Neurolabs improved synthetic data generation with Encord Active.   The importance of photorealistic synthetic data extends further, as it offers broader access to high-quality data needed for deep learning. Its impact can be seen across various domains, from improving computer vision to enhancing natural language processing. By utilizing photorealistic synthetic data, previously challenging breakthroughs become feasible, leading to the development of more robust and versatile AI systems. This democratization of data aids in the creation of AI models that excel not only in controlled environments but also in the complex and ever-changing real world. In this way, photorealistic synthetic data contributes to the ongoing growth and evolution of AI technology. Photorealistic Unreal Graphics (PUG) Environments Utilizing the robust capabilities of the Unreal Engine, Photorealistic Unreal Graphics (PUG) environments serve as dynamic canvases where AI models can be crafted, tested, and refined with unprecedented precision and realism. A distinguishing feature of PUG environments is their integration of photorealism with highly detailed control, achieved by incorporating a diverse collection of 3D assets, including objects and backgrounds, within the Unreal Engine framework. They provide researchers with the ability to arrange and modify scenes, parameters, and variables, all manageable through the WebRTC packet-based system. The incorporation of the TorchMultiverse python library further simplifies this process, allowing researchers to seamlessly configure scenes, request specific image data, and propel experimentation to new heights.   Photorealistic Unreal Graphics (PUG) Although initially centered on static image datasets, the potential of PUG environments reaches far beyond this scope. They provide a dynamic communication channel that facilitates active learning scenarios, real-time adaptation, and even video rendering, fundamentally transforming how AI models engage with and learn from their surroundings. In essence, PUG environments transcend the boundaries of traditional data by seamlessly blending realism and control. As the field of artificial intelligence continues to evolve, these environments become essential instruments in understanding how AI models react, learn, and adapt to a wide array of situations. Photorealistic Unreal Graphics (PUG): Dataset Family The photorealistic unreal graphics (PUG) dataset family is a series of meticulously curated datasets. Photorealistic Unreal Graphics (PUG): Animals PUG: Animals is the leading dataset of the PUG dataset family, consisting of over 215,000 images that include 70 animal assets, 64 backgrounds, 3 object sizes, 4 textures, and 4 camera orientations.  Photorealistic Unreal Graphics (PUG): Animals Dataset This dataset serves as a vital tool for exploring out-of-distribution (OOD) generalization, offering researchers the ability to meticulously control distribution shifts during training and testing scenarios. Photorealistic Unreal Graphics (PUG): ImageNet PUG: ImageNet serves as a robust benchmark for image classifiers. The dataset contains 88,328 images, each meticulously rendered using a collection of 724 assets representing 151 ImageNet classes. Photorealistic Unreal Graphics (PUG): ImageNet Dataset It provides a challenging benchmark for assessing the robustness of image classifiers, enabling ML researchers a deeper understanding of the model’s performance across a spectrum of factors, such as pose, texture, size, and lighting. Photorealistic Unreal Graphics (PUG): SPAR PUG: SPAR functions as a key benchmark for vision-language models (VLMs). With 43,560 images, SPAR offers a comprehensive platform for testing VLMs across a range of scene recognition, object recognition, and position detection tasks. This dataset introduces a fresh perspective on evaluating VLMs, enabling a systematic evaluation of their capabilities and exposing areas in need of refinement.  Photorealistic Unreal Graphics (PUG): SPAR Dataset Photorealistic Unreal Graphics (PUG): AR4T PUG: AR4T serves as a supplementary fine-tuning dataset for VLMs, working in conjunction with PUG: SPAR. This dataset offers a unique process to address VLM’s struggles with spatial relations and attributes. With its photorealistic nature, PUG: AR4T bridges the gap between synthetic data and real-world capability, enabling improved understanding and performance. Find the links to download the PUG datasets in their GitHub.   Photorealistic Unreal Graphics (PUG): Key Takeaways PUG Dataset Family: Meta AI's FAIR team introduces the Photorealistic Unreal Graphics (PUG) dataset family, comprising Animals, ImageNet, SPAR, and AR4T datasets, fueling representation learning research with meticulously curated images. Revolutionizing AI Experimentation: PUG environments leverage Unreal Engine's power, offering unprecedented realism and control for crafting, testing, and refining AI models. These environments enable active learning, real-time adaptation, and video rendering. Photorealistic Synthetic Data's Impact: Photorealistic synthetic data bridges the gap between simulation and reality, offering fine-grained control over factors like lighting and textures. This approach democratizes access to high-quality data for diverse AI domains, from computer vision to natural language processing. Diverse Benchmarking: PUG datasets redefine benchmarks for various AI tasks. PUG: Animals for out-of-distribution generalization, PUG: ImageNet for image classifier robustness, PUG: SPAR for vision-language models, and PUG: AR4T for VLM fine-tuning, collectively advancing AI research and innovation. Read the original paper by Florian Bordes, Shashank Shekhar, Mark Ibrahim, Diane Bouchacourt, Pascal Vincent, Ari S. Morcos on Arxiv: PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning.   Other Recent Releases by Meta AI: Llama 2: Meta AI's Latest Open Source Large Language Model LLM Meta AI’s I-JEPA, Image-based Joint-Embedding Predictive Architecture Meta AI's New Breakthrough: Segment Anything Model (SAM) ImageBind MultiJoint Embedding Model from Meta

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ML Monitoring vs. ML Observability

Picture this: you've developed an ML system that excels in domain X, performing task Y. It's all smooth sailing as you launch it into production, confident in its abilities. But suddenly, customer complaints start pouring in, signaling your once-stellar model is now off its game. Not only does this shake your company's reputation, but it also demands quick fixes. The catch? Sorting out these issues at the production level is a major headache. What could've saved the day? Setting up solid monitoring and observability right from the get-go to catch anomalies and outliers before they spiral out of control. Fast forward to today, AI and ML are everywhere, revolutionizing industries by extracting valuable insights, optimizing operations, and guiding data-driven decisions. However, these advancements necessitate a proactive approach to ensure timely anomaly and outlier detection for building reliable, efficient, and transparent models. That's where ML monitoring and observability come to the rescue, playing vital roles in developing trustworthy AI systems. In this article, we'll uncover the crucial roles of ML monitoring and ML observability in crafting dependable AI-powered systems. We'll explore their key distinctions and how they work together to ensure reliability in AI deployments. What is ML Monitoring?  Machine learning monitoring refers to the continuous and systematic process of tracking a machine learning model’s performance, behavior, and health from development to production. It encompasses collecting, analyzing, and interpreting various metrics, logs, and data generated by ML systems to ensure optimal functionality and prompt detection of potential issues or anomalies. ML Monitoring Framework ML monitoring detects and tracks: Metrics like accuracy, precision, recall, F1 score, etc. Changes in input data distributions, known as data drift Instances when a model's performance declines or degrades Anomalies and outliers in model behavior or input data Model latency Utilization of computational resources, memory, and other system resources Data quality problems in input datasets, such as missing values or incorrect labels, that can negatively impact model performance Bias and fairness of models Model versions Breaches and unauthorized access attempts Interested in learning more about bias in machine learning models? Read our comprehensive guide on Mitigating Bias In Machine Learning Models Objectives A Machine Learning Model Monitoring Checklist ML monitoring involves the continuous observation, analysis, and management of various aspects of ML systems to ensure they are functioning as intended and delivering accurate outcomes. It primarily focuses on the following: Model Performance Tracking: Helps machine learning practitioners and stakeholders understand how well a model fares with new data and whether the predictive accuracy aligns with the intended goals. Early Anomaly Detection: Involves continuously analyzing model behavior to promptly detect any deviations from expected performance. This early warning system helps identify potential issues, such as model degradation, data drift, or outliers, which, if left unchecked, could lead to significant business and operational consequences. Root-Cause Analysis: Identifying the fundamental reason behind issues in the model or ML pipeline. This enables data scientists and ML engineers to pinpoint the root causes of problems, leading to more efficient debugging and issue resolution. Diagnosis: Examines the identified issues to understand their nature and intricacies, which assists in devising targeted solutions for smoother debugging and resolution. Model Governance: Establishes guidelines and protocols to oversee model development, deployment, and maintenance. These guidelines ensure ML models are aligned with organizational standards and objectives. Compliance: Entails adhering to legal and ethical regulations. ML monitoring ensures that the deployed models operate within defined boundaries and uphold the necessary ethical and legal standards. Check out our curated list of Top Tools for Outlier Detection in Computer Vision. Importance of Monitoring in Machine Learning ML monitoring is significant for several reasons, including: Proactive Anomaly Resolution: Early detection of anomalies through ML monitoring enables data science teams to take timely actions and address issues before they escalate. This proactive approach helps prevent significant business disruptions and customer dissatisfaction, especially in mission-critical industries. Data Drift Detection: ML monitoring helps identify data drift, where the input data distribution shifts over time. If a drift is detected, developers can take prompt action to update and recalibrate the model, ensuring its accuracy and relevance to the changing data patterns. Continuous Improvement: ML Monitoring ensures iterative model improvement by providing feedback on model behavior. This feedback loop supports refining ML algorithms and strategies, leading to enhanced model performance over time. Risk Mitigation: ML monitoring helps mitigate risks associated with incorrect predictions or erroneous decisions, which is especially important in industries such as healthcare and finance, where model accuracy is critical.  Performance Validation: Monitoring provides valuable insights into model performance in production environments, ensuring that they continue to deliver reliable results in real-word applications. To achieve this, monitoring employs various techniques, such as cross-validation and A/B testing, which help assess model generalization and competence in dynamic settings. What is ML Observability?  ML Observability Machine learning observability is an important practice that provides insights into the inner workings of ML data pipelines and system well-being. It involves understanding decision-making, data flow, and interactions within the ML pipeline. As ML systems become more complex, so does observability due to multiple interacting components such as data pipelines, model notebooks, cloud setups, containers, distributed systems, and microservices. According to Gartner, by 2026, 70% of organizations effectively implementing observability will attain quicker decision-making, granting a competitive edge in business or IT processes. ML observability detects: Model behavior during training, inference, and decision-making processes Data flow through the ML pipeline, including preprocessing steps and data transformations Feature importance and their contributions to model predictions Model profiling Model performance metrics, such as accuracy, precision, recall, and F1 score Utilization of computational resources, memory, and processing power by ML models Bias in ML models Anomalies and outliers in model behavior or data. Model drift, data drift, and concept drift occur when model behavior or input data changes over time Overall performance of the ML system, including response times, latency, and throughput Model error analysis Model explainability and interpretability  Model versions and their performance using production data Objectives The primary objectives of ML observability are: Transparency and Understandability: ML observability aims to provide transparency into the black-box nature of ML models. By gaining a deeper understanding of model behavior, data scientists can interpret model decisions and build trust in the model's predictions. Root Cause Analysis: ML observability enables thorough root cause analysis when issues arise in the ML pipeline. By tracing back the sequence of events and system interactions, ML engineers can pinpoint the root causes of problems and facilitate effective troubleshooting. Data Quality Assessment: ML observability seeks to monitor data inputs and transformations to identify and rectify data quality issues that may adversely affect model performance. Performance Optimization: With a holistic view of the system's internal dynamics, ML observability aims to facilitate the optimization of model performance and resource allocation to achieve better results. Importance of Observability in Machine Learning ML observability plays a pivotal role in AI and ML, offering crucial benefits such as: Continuous Improvement: ML observability offers insights into model behavior to help refine algorithms, update models, and continuously enhance their predictive capabilities. Proactive Problem Detection: ML observability continuously observes model behavior and system performance to address potential problems before they escalate. Real-time Decision Support: ML observability offers real-time insights into model performance, enabling data-driven decision-making in dynamic and rapidly changing environments. Building Trust in AI Systems: ML observability fosters trust in AI systems. Understanding how models arrive at decisions provides confidence in the reliability and ethics of AI-driven outcomes. Compliance and Accountability: In regulated industries, ML observability helps maintain compliance with ethical and legal standards. Understanding model decisions and data usage ensure models remain accountable and within regulatory bounds. ML Monitoring vs. ML Observability: Overlapping Elements Both monitoring and observability are integral components of ML OPs that work in tandem to ensure the seamless functioning and optimization of ML models. Although they have distinct purposes, there are essential overlaps where their functions converge, boosting the overall effectiveness of the ML ecosystem. Some of their similar elements include: Anomaly Detection Anomaly detection is a shared objective in both ML monitoring and observability. Monitoring systems and observability tools are designed to identify deviations in model behavior and performance that may indicate potential issues or anomalies.    Data Quality Control Ensuring data quality is essential for robust ML operations, and both monitoring and observability contribute to this aspect. ML monitoring systems continuously assess the quality and integrity of input data, monitoring for data drift or changes in data distribution that could impact model performance. Similarly, observability tools enable data scientists to gain insights into the characteristics of input data and assess its suitability for training and inference. Real-time Alerts Real-time alerts are a shared feature of both ML monitoring and observability. When critical issues or anomalies are detected, these systems promptly trigger alerts, notifying relevant stakeholders for immediate action to minimize outages.  Continuous ML Improvement ML monitoring and observability foster a culture of ongoing improvement in machine learning. Monitoring identifies issues like performance drops, prompting iterative model refinement. Observability offers insights into system behavior, aiding data-driven optimization and enhanced decisions. Model Performance Assessment Evaluating model performance is a fundamental aspect shared by both monitoring and observability. Monitoring systems track model metrics over time, allowing practitioners to assess performance trends and benchmark against predefined thresholds. Observability complements this by offering a comprehensive view of the ML pipeline, aiding in the identification of potential bottlenecks or areas of improvement that may affect overall model performance. ML Monitoring vs. ML Observability: Key Differences ML Monitoring vs. ML Observability While ML monitoring and ML observability share common goals in ensuring effective machine learning operationalization, they differ significantly in their approaches, objectives, and the scope of insights they provide. Area of Focus The primary distinction lies in their focus. ML monitoring primarily answers "what" is happening within the ML system, tracking key metrics and indicators to identify deviations and issues. On the other hand, ML observability delves into the "why" and "how" aspects, providing in-depth insights into the internal workings of the ML pipeline. Its goal is to provide a deeper understanding of the model's behavior and decision-making process. Objectives ML monitoring's main objective is to track model performance, identify problem areas, and ensure operational stability. It aims to validate that the model is functioning as expected and provides real-time alerts to address immediate concerns. In contrast, ML observability primarily aims to offer holistic insights into the ML system's health. This involves identifying systemic issues, data quality problems, and shedding light on the broader implications of model decisions. Approach ML monitoring is a failure-centric practice, designed to detect and mitigate failures in the ML model. It concentrates on specific critical issues that could lead to incorrect predictions or system downtime. In contrast, ML observability pursues a system-centric approach, analyzing the overall system health, including data flows, dependencies, and external factors that denote the system's behavior and performance. Perspective ML Monitoring typically offers an external, high-level view of the ML model, focusing on metrics and performance indicators visible from the outside. ML Observability, on the other hand, offers a holistic view of the ML system inside and out. It provides insights into internal states, algorithmic behavior, and the interactions between various components, leading to an in-depth awareness of the system's dynamics. Performance Analytics ML monitoring relies on historical metrics data to analyze model performance and identify trends over time. ML observability, in contrast, emphasizes real-time analysis, allowing data scientists and engineers to explore the model's behavior in the moment, thereby facilitating quicker and more responsive decision-making. Use Case ML monitoring is particularly valuable in scenarios where immediate detection of critical issues is essential, such as in high-stakes applications like healthcare and finance. ML observability, on the other hand, shines in complex, large-scale ML systems where understanding the intricate interactions between various components and identifying systemic issues are crucial. To exemplify this difference, consider a medical AI company analyzing chest x-rays. Monitoring might signal a performance metric decline over the past month. Meanwhile, ML observability can detect that a new hospital joined the system, introducing different image sources and affecting features, underscoring the significance of systemic insights in intricate, large-scale ML systems. Encord Active: Empowering Robust ML Development With Monitoring & Observability Encord Active is an open-source ML platform that is built to revolutionize the process of building robust ML models. With its comprehensive suite of end-to-end monitoring and observability features, Encord Active equips practitioners with the essential tools to elevate their ML development journey.  Encord Active Prominent features include intuitive dashboards for performance assessment. These dashboards facilitate the monitoring of performance metrics and visualization of feature distributions. It also offers automated robustness tests for mitigation, detects biases for ethical outcomes, and enables comprehensive evaluations for effective comparisons. Additionally, auto-identification of labeling errors ensures reliable results. ML Observability vs. ML Monitoring: Key Takeaways Effective ML monitoring and ML observability are crucial for developing and deploying successful machine learning models.  ML monitoring components, such as real-time alerts and metrics collection, ensure continuous tracking of model performance and prompt issue identification. ML observability components, such as root cause analysis and model fairness assessment, provide detailed insights into the ML system's behavior and enable proactive improvements for long-term system reliability. The combination of ML monitoring and ML observability enables proactive issue detection and continuous improvement, leading to optimized ML systems. Together, ML monitoring and ML observability play a pivotal role in achieving system health, mitigating bias, and supporting real-time decision-making. Organizations can rely on both practices to build robust and trustworthy AI-driven solutions and drive innovation.

Aug 15 2023

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Model Inference in Machine Learning

Today, machine learning (ML)-based forecasting has become crucial across various industries. It plays a pivotal role in automating business processes, delivering personalized user experiences, gaining a competitive advantage, and enabling efficient decision-making. A key component that drives decisions for ML systems is model inference. In this article, we will explain the concept of machine learning inference, its benefits, real-world applications, and the challenges that come with its implementation, especially in the context of responsible artificial intelligence practices. What is Model Inference in Machine Learning? Model inference in machine learning refers to the operationalization of a trained ML model, i.e., using an ML model to generate predictions on unseen real-world data in a production environment. The inference process includes processing incoming data and producing results based on the patterns and relationships learned during the machine learning training phase. The final output could be a classification label, a regression value, or a probability distribution over different classes. An inference-ready model is optimized for performance, efficiency, scalability, latency, and resource utilization. The model must be optimized to run efficiently on the chosen target platform to ensure that it can handle large volumes of incoming data and generate predictions promptly. This requires selecting appropriate hardware or cloud infrastructure for deployment, typically called an ML inference server. There are two common ways of performing inference:  Batch inference: Model predictions are generated on a chunk of observations after specific intervals. It is best-suited for low latency tasks, such as analyzing historical data.  Real-time inference: Predictions are generated instantaneously as soon as new data becomes available. It is best-suited for real-time decision-making in mission-critical applications. To illustrate model inference in machine learning, consider an animal image classification task, i.e., a trained convolutional neural network (CNN) used to classify animal images into various categories (e.g., cats, dogs, birds, and horses). When a new image is fed into the model, it extracts and learns relevant features, such as edges, textures, and shapes. The final layer of the model provides the probability scores for each category. The category with the highest probability is considered the model's prediction for that image, indicating whether it is a cat, dog, bird, or horse. Such a model can be valuable for various applications, including wildlife monitoring, pet identification, and content recommendation systems. Some other common examples of machine learning model inference include predicting whether an email is spam or not, identifying objects in images, or determining sentiment in customer reviews. Benefits of ML Model Inference Let’s discuss in detail how model inference in machine learning impacts different aspects of business. Real-Time Decision-Making Decisions create value – not data. Model inference facilitates real-time decision-making across several verticals, especially vital in critical applications such as autonomous vehicles, fraud detection, and healthcare. These scenarios demand immediate and accurate predictions to ensure safety, security, and timely action. A couple of examples of how ML model inference facilitates decision-making: Real-time model inference for weather forecasting based on sensor data enables geologists, meteorologists, and hydrologists to accurately predict environmental catastrophes like floods, storms, and earthquakes. In cybersecurity, ML models can accurately infer malicious activity, enabling network intrusion detection systems to actively respond to threats and block unauthorized access. Automation & Efficiency Model inference significantly reduces the need for manual intervention and streamlines operations across various domains. It allows businesses to take immediate actions based on real-time insights. For instance: In customer support, chatbots powered by ML model inference provide automated responses to user queries, resolving issues promptly and improving customer satisfaction. In enterprise environments, ML model inference powers automated anomaly detection systems to identify, rank, and group outliers based on large-scale metric monitoring. In supply chain management,real-timemodel inference helps optimize inventory levels, ensuring the right products are available at the right time, thus reducing costs and minimizing stockouts. Personalization Personalized Recommendation System Compared to Traditional Recommendation Model inference enables businesses to deliver personalized user experiences, catering to individual preferences and needs. For instance: ML-based recommendation systems, such as those used by streaming platforms, e-commerce websites, and social media platforms, analyze user behavior in real-time to offer tailored content and product recommendations. This personalization enhances user engagement and retention, leading to increased customer loyalty and higher conversion rates. Personalized marketing campaigns based on ML inference yield better targeting and improved customer response rates. Scalability & Cost-Efficiency End-to-end Scalable Machine Learning Pipeline By leveraging cloud infrastructure and hardware optimization, organizations can deploy ML applications cost-efficiently. Cloud-based model inference with GPU support allows organizations to scale with rapid data growth and changing user demands. Moreover, it eliminates the need for on-premises hardware maintenance, reducing capital expenditures and streamlining IT management. Cloud providers also offer specialized hardware-optimized inference services at a low cost. Furthermore, on-demand serverless inference enables organizations to automatically manage and scale workloads that have low or inconsistent traffic. With such flexibility, businesses can explore new opportunities and expand operations into previously untapped markets. Real-time insights and accurate predictions empower organizations to enter new territories with confidence, informed by data-driven decisions. Real-World Use Cases of Model Inference AI Technology Landscape Model inference in machine learning finds extensive application across various industries, driving transformative changes and yielding valuable insights. Below, we delve into each real-world use case, exploring how model inference brings about revolutionary advancements: Healthcare & Medical Diagnostics Model inference is revolutionizing medical diagnostics through medical image analysis and visualization. Trained deep learning models can accurately interpret medical images, such as X-rays, MRIs, and CT scans, to aid in disease diagnosis. By analyzing the intricate details in medical images, model inference assists radiologists and healthcare professionals in identifying abnormalities, enabling early disease detection and improving patient outcomes. Real-time monitoring of patient vital signs using sensor data from medical Internet of Things (IoT) devices and predictive models helps healthcare professionals make timely interventions and prevent critical events. Natural Language Processing (NLP) models process electronic health records and medical literature, supporting clinical decision-making and medical research. Natural Language Processing (NLP) Model inference plays a pivotal role in applications of natural language processing (NLP), such as chatbots and virtual assistants. NLP models, often based on deep learning architectures like recurrent neural networks (RNNs), long short-term memory networks (LSTMs), or transformers, enable chatbots and virtual assistants to understand and respond to user queries in real-time. By analyzing user input, NLP models can infer contextually relevant responses, simulating human-like interactions. This capability enhances user experience and facilitates efficient customer support, as chatbots can handle a wide range of inquiries and provide prompt responses 24/7. Autonomous Vehicles Model inference is the backbone of decision-making in computer vision tasks like autonomous vehicle driving and detection. Trained machine learning models process data from sensors like LiDAR, cameras, and radar in real-time to make informed decisions on navigation, collision avoidance, and route planning. In autonomous vehicles, model inference occurs rapidly, allowing vehicles to respond instantly to changes in their environment. This capability is critical for ensuring the safety of passengers and pedestrians, as the vehicle must continuously assess its surroundings and make split-second decisions to avoid potential hazards. Fraud Detection In the financial and e-commerce sectors, model inference is used extensively for fraud detection. Machine learning models trained on historical transaction data can quickly identify patterns indicative of fraudulent activities in real-time. By analyzing incoming transactions as they occur, model inference can promptly flag suspicious transactions for further investigation or block fraudulent attempts. Real-time fraud detection protects businesses and consumers alike, minimizing financial losses and safeguarding sensitive information. So, model interference can be used in horizontal and vertical B2B marketplaces, as well as in the B2C sector. Environmental Monitoring Model inference finds applications in environmental data analysis, enabling accurate and timely monitoring of environmental conditions. Models trained on historical environmental data, satellite imagery, and other relevant information can predict changes in air quality, weather patterns, or environmental parameters. By deploying these models for real-time inference, organizations can make data-driven decisions to address environmental challenges, such as air pollution, climate change, or natural disasters. The insights obtained from model inference aid policymakers, researchers, and environmentalists in developing effective strategies for conservation and sustainable resource management. Interested in learning more about ML-based environmental protection? Read how Encord has helped in Saving the Honey Bees with Computer Vision. Financial Services In the finance sector, ML model inference plays a crucial role in enhancing credit risk assessment. Trained machine learning models analyze vast amounts of historical financial data and loan applications to predict the creditworthiness of potential borrowers accurately. Real-time model inference allows financial institutions to swiftly evaluate credit risk and make informed lending decisions, streamlining loan approval processes and reducing the risk of default. Algorithmic trading models use real-time market data to make rapid trading decisions, capitalizing on market opportunities with dependencies. Moreover, model inference aids in determining optimal pricing strategies for financial products. By analyzing market trends, customer behavior, and competitor pricing, financial institutions can dynamically adjust their pricing to maximize profitability while remaining competitive. Customer Relationship Management In customer relationship management (CRM), model inference powers personalized recommendations to foster stronger customer engagement, increase customer loyalty, and drive recurring business. By analyzing customer behavior, preferences, and purchase history, recommendation systems based on model inference can suggest products, services, or content tailored to individual users. They contribute to cross-selling and upselling opportunities, as customers are more likely to make relevant purchases based on their interests. Moreover, customer churn prediction models help businesses identify customers at risk of leaving and implement targeted retention strategies. Sentiment analysis models analyze customer feedback to gauge satisfaction levels and identify areas for improvement. Predictive Maintenance in Manufacturing Model inference is a game-changer in predictive maintenance for the manufacturing industry. By analyzing real-time IoT sensor data from machinery and equipment, machine learning models can predict equipment failures before they occur. This capability allows manufacturers to schedule maintenance activities proactively, reducing downtime and preventing costly production interruptions. As a result, manufacturers can extend the lifespan of their equipment, improve productivity, and overall operational efficiency. Limitations of Machine Learning Model Inference  Model inference in machine learning brings numerous benefits, but it also presents various challenges that must be addressed for successful and responsible AI deployment. In this section, we delve into the key challenges and the strategies to overcome them: Infrastructure Cost & Resource Intensive Model inference can be resource-intensive, particularly for complex models and large datasets. Deploying models on different hardware components, such as CPUs, GPUs, TPUs, FPGAs, or custom AI chips, poses challenges in optimizing resource allocation and achieving cost-effectiveness. High computational requirements result in increased operational costs for organizations. To address these challenges, organizations must carefully assess their specific use case and the model's complexity. Choosing the right hardware and cloud-based solutions can optimize performance and reduce operational costs. Cloud services offer the flexibility to scale resources as needed, providing cost-efficiency and adaptability to changing workloads. Latency & Interoperability Real-time model inference demands low latency to provide immediate responses, especially for mission-critical applications like autonomous vehicles or healthcare emergencies. In addition, models should be designed to run on diverse environments, including end devices with limited computational resources. To address latency concerns, efficient machine learning algorithms and their optimization are crucial. Techniques such as quantization, model compression, and pruning can reduce the model's size and computational complexity without compromising model accuracy. Furthermore, using standardized model formats like ONNX (Open Neural Network Exchange) enables interoperability across different inference engines and hardware. Ethical Frameworks Model inference raises ethical implications, particularly when dealing with sensitive data or making critical decisions that impact individuals or society. Biased or discriminatory predictions can have serious consequences, leading to unequal treatment. To ensure fairness and unbiased predictions, organizations must establish ethical guidelines in the model development and deployment process. Promoting responsible and ethical AI practices involves fairness-aware training, continuous monitoring, and auditing of model behavior to identify and address biases. Model interpretability and transparency are essential to understanding how decisions are made, particularly in critical applications like healthcare and finance. Transparent Model Development Complex machine learning models can act as "black boxes," making it challenging to interpret their decisions. However, in critical domains like healthcare and finance, interpretability is vital for building trust and ensuring accountable decision-making. To address this challenge, organizations should document the model development process, including data sources, preprocessing steps, and model architecture. Additionally, adopting explainable AI techniques like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) can provide insights into how the model arrives at its decisions, making it easier to understand and interpret its behavior. Want to build transparent AI systems that comply with the latest regulations? Read our blog post: What the European AI Act Means for You, AI Developer. Robust Model Training & Testing During model training, overfitting is a common challenge, where the model performs well on the training data but poorly on unseen data. Overfitting can result in inaccurate predictions and reduced generalization. To address overfitting, techniques like regularization, early stopping, and dropout can be applied during model training. Data augmentation is another useful approach, i.e., introducing variations in the training data to improve the model's ability to generalize on unseen data. Want to learn more about handling ML datasets? Read our detailed guides on Introduction to Balanced and Imbalanced Datasets in Machine Learning and Training, Validation, Test Split for Machine Learning Datasets. Furthermore, the accuracy of model predictions heavily depends on the quality and representativeness of the training data. Addressing biased or incomplete data is crucial to prevent discriminatory predictions and ensure fairness. Additionally, models must be assessed for resilience against adversarial attacks and input variations. Adversarial attacks involve intentionally perturbing input data to mislead the model's predictions. Robust models should be able to withstand such attacks and maintain accuracy. Continuous Monitoring & Retraining Models may experience a decline in performance over time due to changing data distributions. Continuous monitoring of model performance is essential to detect degradation and trigger retraining when necessary. Continuous monitoring involves tracking model performance metrics and detecting instances of data drift. When data drift is identified, models can be retrained on the updated data to ensure their accuracy and relevance in dynamic environments. Security & Privacy Protection Model inference raises concerns about data and model security in real-world applications. Typically, four types of attacks can occur during inference: membership inference attacks, model extraction attacks, property inference attacks, and model inversion attacks. Hence, sensitive data processed by the model must be protected from unauthorized access and potential breaches. Ensuring data security involves implementing robust authentication and encryption mechanisms. Techniques like differential privacy and federated learning can enhance privacy protection in machine learning models. Additionally, organizations must establish strong privacy measures for handling sensitive data, adhering to regulations such as GDPR (General Data Protection Regulation), HIPAA (Health Insurance Portability and Accountability Act), and SOC 2. Disaster Recovery In cloud-based model inference, robust security measures and data protection are essential to prevent data loss and ensure data integrity and availability, particularly for mission-critical applications. Disaster recovery plans should be established to handle potential system failures, data corruption, or cybersecurity threats. Regular data backups, failover mechanisms, and redundancy can mitigate the impact of unforeseen system failures. Popular Tools for ML Model Inference Data scientists, ML engineers, and AI practitioners typically use programming languages like Python and R to build AI systems. Python, in particular, offers a wide range of libraries and frameworks like scikit-learn, PyTorch, Keras, and TensorFlow. Practitioners also employ tools like Docker and Kubernetes to enable the containerization of machine learning tasks. Additionally, APIs (Application Programming Interfaces) play a crucial role in enabling seamless integration of machine learning models into applications and services. There are several popular tools and frameworks available for  model inference in machine learning: Amazon SageMaker: Amazon SageMaker is a fully managed service that simplifies model training and deployment on the Amazon Web Services (AWS) cloud platform. It allows easy integration with popular machine learning frameworks, enabling seamless model inference at scale. TensorFlow Serving: TensorFlow Serving is a dedicated library for deploying TensorFlow models for inference. It supports efficient and scalable serving of machine learning models in production environments. Triton Inference Server: Triton Inference Server, developed by NVIDIA, is an open-source server for deploying machine learning models with GPU support. Check out our curated list of Best Image Annotation Tools for Computer Vision. Model Inference in Machine Learning: Key Takeaways Model inference is a pivotal stage in the machine learning lifecycle. This process ensures that the trained models can be efficiently utilized to process real-time data and generate predictions. Real-time model inference empowers critical applications that demand instant decision-making, such as autonomous vehicles, fraud detection, and healthcare emergencies. It offers a wide array of benefits, revolutionizing decision-making, streamlining operations, and enhancing user experiences across various industries. While model inference brings numerous benefits, it also presents challenges that must be addressed for responsible AI deployment. These challenges include high infrastructure costs, ensuring low latency and interoperability, ethical considerations to avoid biased predictions, model transparency for trust and accountability, etc. Organizations must prioritize ethical AI frameworks, robust disaster recovery plans, continuous monitoring, model retraining, and staying vigilant against inference-level attacks to ensure model accuracy, fairness, and resilience in real-world applications. The future lies in creating a harmonious collaboration between AI and human ingenuity, fostering a more sustainable and innovative world where responsible AI practices unlock the full potential of machine learning inference.

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Mastering Data Cleaning & Data Preprocessing

Data quality is paramount in data science and machine learning. The input data quality heavily influences machine learning models' performance. In this context, data cleaning and preprocessing are not just preliminary steps but crucial components of the machine learning pipeline. Data cleaning involves identifying and correcting errors in the dataset, such as dealing with missing or inconsistent data, removing duplicates, and handling outliers. Ensuring you train the machine learning mode on accurate and reliable data is essential. The model may learn from incorrect data without proper cleaning, leading to inaccurate predictions or classifications. On the other hand, data preprocessing is a broader concept that includes data cleaning and other steps to prepare the data for machine learning algorithms. These steps may include data transformation, feature selection, normalization, and reduction. The goal of data preprocessing is to convert raw data into a suitable format that machine learning algorithms can learn. Incorporating data science consulting services can elevate this process, providing expertise to optimize data transformations and ensure that data sets are primed for advanced analytics. The importance of data cleaning and data preprocessing cannot be overstated, as it can significantly impact the model's performance. A well-cleaned and preprocessed dataset can lead to more accurate and reliable machine learning models, while a poorly cleaned and preprocessed dataset can lead to misleading results and conclusions. This guide will delve into the techniques and best data cleaning and data preprocessing practices. You will learn their importance in machine learning, common techniques, and practical tips to improve your data science pipeline. Whether you are a beginner in data science or an experienced professional, this guide will provide valuable insights to enhance your data cleaning and preprocessing skills. Data Cleaning What is Data Cleaning? In data science and machine learning, the quality of input data is paramount. It's a well-established fact that data quality heavily influences the performance of machine learning models. This makes data cleaning, detecting, and correcting (or removing) corrupt or inaccurate records from a dataset a critical step in the data science pipeline. Data cleaning is not just about erasing data or filling in missing values. It's a comprehensive process involving various techniques to transform raw data into a format suitable for analysis. These techniques include handling missing values, removing duplicates, data type conversion, and more. Each technique has its specific use case and is applied based on the data's nature and the analysis's requirements. Common Data Cleaning Techniques Handling Missing Values: Missing data can occur for various reasons, such as errors in data collection or transfer. There are several ways to handle missing data, depending on the nature and extent of the missing values. Imputation: Here, you replace missing values with substituted values. The substituted value could be a central tendency measure like mean, median, or mode for numerical data or the most frequent category for categorical data. More sophisticated imputation methods include regression imputation and multiple imputation. Deletion: You remove the instances with missing values from the dataset. While this method is straightforward, it can lead to loss of information, especially if the missing data is not random. Removing Duplicates: Duplicate entries can occur for various reasons, such as data entry errors or data merging. These duplicates can skew the data and lead to biased results. Techniques for removing duplicates involve identifying these redundant entries based on key attributes and eliminating them from the dataset. Data Type Conversion: Sometimes, the data may be in an inappropriate format for a particular analysis or model. For instance, a numerical attribute may be recorded as a string. In such cases, data type conversion, also known as datacasting, is used to change the data type of a particular attribute or set of attributes. This process involves converting the data into a suitable format that machine learning algorithms can easily process. Outlier Detection: Outliers are data points that significantly deviate from other observations. They can be caused by variability in the data or errors. Outlier detection techniques are used to identify these anomalies. These techniques include statistical methods, such as the Z-score or IQR method, and machine learning methods, such as clustering or anomaly detection algorithms. Interested in outlier detection? Read Top Tools for Outlier Detection in Computer Vision.   Data cleaning is a vital step in the data science pipeline. It ensures that the data used for analysis and modeling is accurate, consistent, and reliable, leading to more robust and reliable machine learning models. Remember, data cleaning is not a one-size-fits-all process. The techniques used will depend on the nature of the data and the specific requirements of the analysis or model.    Data Preprocessing What is Data Preprocessing? Data preprocessing is critical in data science, particularly for machine learning applications. It involves preparing and cleaning the dataset to make it more suitable for machine learning algorithms. This process can reduce complexity, prevent overfitting, and improve the model's overall performance. The data preprocessing phase begins with understanding your dataset's nuances and the data's main issues through Exploratory Data Analysis. Real-world data often presents inconsistencies, typos, missing data, and different scales. You must address these issues to make the data more useful and understandable. This process of cleaning and solving most of the issues in the data is what we call the data preprocessing step. Skipping the data preprocessing step can affect the performance of your machine learning model and downstream tasks. Most models can't handle missing values, and some are affected by outliers, high dimensionality, and noisy data. By preprocessing the data, you make the dataset more complete and accurate, which is critical for making necessary adjustments in the data before feeding it into your machine learning model. Data preprocessing techniques include data cleaning, dimensionality reduction, feature engineering, sampling data, transformation, and handling imbalanced data. Each of these techniques has its own set of methods and approaches for handling specific issues in the data. Common Data Preprocessing Techniques Data Scaling Data scaling is a technique used to standardize the range of independent variables or features of data. It aims to standardize the data's range of features to prevent any feature from dominating the others, especially when dealing with large datasets. This is a crucial step in data preprocessing, particularly for algorithms sensitive to the range of the data, such as deep learning models. There are several ways to achieve data scaling, including Min-Max normalization and Standardization. Min-Max normalization scales the data within a fixed range (usually 0 to 1), while Standardization scales data with a mean of 0 and a standard deviation of 1. Encoding Categorical Variables Machine learning models require inputs to be numerical. If your data contains categorical data, you must encode them to numerical values before fitting and evaluating a model. This process, known as encoding categorical variables, is a common data preprocessing technique. One common method is One-Hot Encoding, which creates new binary columns for each category/label in the original columns. Data Splitting Data Splitting is a technique to divide the dataset into two or three sets, typically training, validation, and test sets. You use the training set to train the model and the validation set to tune the model's parameters. The test set provides an unbiased evaluation of the final model. This technique is essential when dealing with large data, as it ensures the model is not overfitted to a particular subset of data. For more details on data splitting, read Training, Validation, Test Split for Machine Learning Datasets. Handling Missing Values Missing data in the dataset can lead to misleading results. Therefore, it's essential to handle missing values appropriately. Techniques for handling missing values include deletion, removing the rows with missing values, and imputation, replacing the missing values with statistical measures like mean, median, or model. This step is crucial in ensuring the quality of data used for training machine learning models. Feature Selection Feature selection is a process in machine learning where you automatically select those features in your data that contribute most to the prediction variable or output in which you are interested. Having irrelevant features in your data can decrease the accuracy of many models, especially linear algorithms like linear and logistic regression. This process is particularly important for data scientists working with high-dimensional data, as it reduces overfitting, improves accuracy, and reduces training time. Three benefits of performing feature selection before modeling your data are: Reduces Overfitting: Less redundant data means less opportunity to make noise-based decisions. Improves Accuracy: Less misleading data means modeling accuracy improves. Reduces Training Time: Fewer data points reduce algorithm complexity, and it trains faster. Data Cleaning Process Data cleaning, a key component of data preprocessing, involves removing or correcting irrelevant, incomplete, or inaccurate data. This process is essential because the quality of the data used in machine learning significantly impacts the performance of the models. Step-by-Step Guide to Data Cleaning Following these steps ensures your data is clean, reliable, and ready for further preprocessing steps and eventual analysis. Identifying and Removing Duplicate or Irrelevant Data: Duplicate data can arise from various sources, such as the same individual participating in a survey multiple times or redundant fields in the data collection process. Irrelevant data refers to information you can safely remove because it is not likely to contribute to the model's predictive capacity. This step is particularly important when dealing with large datasets. Fixing Syntax Errors: Syntax errors can occur due to inconsistencies in data entry, such as date formats, spelling mistakes, or grammatical errors. You must identify and correct these errors to ensure the data's consistency. This step is crucial in maintaining the quality of data. Filtering out Unwanted Outliers: Outliers, or data points that significantly deviate from the rest of the data, can distort the model's learning process. These outliers must be identified and handled appropriately by removal or statistical treatment. This process is a part of data reduction. Handling Missing Data: Missing data is a common issue in data collection. Depending on the extent and nature of the missing data, you can employ different strategies, including dropping the data points or imputing missing values. This step is especially important when dealing with large data. Validating Data Accuracy: Validate the accuracy of the data through cross-checks and other verification methods. Ensuring data accuracy is crucial for maintaining the reliability of the machine-learning model. This step is particularly important for data scientists as it directly impacts the model's performance. Best Practices for Data Cleaning Here are some practical tips and best practices for data cleaning: Maintain a strict data quality measure while importing new data. Use efficient and accurate algorithms to fix typos and fill in missing regions. Validate data accuracy with known factors and cross-checks. Remember that data cleaning is not a one-time process but a continuous one. As new data comes in, it should also be cleaned and preprocessed before being used in the model. By following these practices, we can ensure that our data is clean and structured to maximize the performance of our machine-learning models. Tools and Libraries for Data Cleaning Various tools and libraries have been developed to aid this process, each with unique features and advantages. One of the most popular libraries for data cleaning is Pandas in Python. This library provides robust data structures and functions for handling and manipulating structured data making it an essential tool for anyone pursuing a Data Science online course. It offers a wide range of functionalities for data cleaning, including handling missing values, removing duplicates, and standardizing data. For instance, Pandas provides functions such as `dropna()` for removing missing values and `drop_duplicates()` for removing duplicate entries. It also offers functions like quantile() for handling outliers and MinMaxScaler() and StandardScaler() for data standardization. The key to effective data cleaning is understanding your data and its specific cleaning needs. Tools like Pandas provide a wide range of functionalities, but applying them effectively is up to you. Another useful tool for data cleaning is the DataHeroes library, which provides a CoresetTreeServiceLG class optimized for data cleaning. This tool computes an "Importance" metric for each data sample, which can help identify outliers and fix mislabeling errors, thus validating the dataset. The FuzzyWuzzy library in Python can be used for fuzzy matching to identify and remove duplicates that may not be exact matches due to variations in data entry or formatting inconsistencies. Real-World Applications of Data Cleaning and Data Preprocessing Data cleaning and data preprocessing in data science are theoretical concepts and practical necessities. They play a pivotal role in enhancing the performance of machine learning models across various industries. Let's delve into some real-world examples that underscore their significance. Improving Customer Segmentation in Retail One of the most common data cleaning and preprocessing applications is in the retail industry, particularly in customer segmentation. Retailers often deal with vast amounts of customer data, which can be messy and unstructured. They can ensure the data's quality by employing data-cleaning techniques such as handling missing values, removing duplicates, and correcting inconsistencies. When preprocessed through techniques like normalization and encoding, this cleaned data can significantly enhance the performance of machine learning models for customer segmentation, leading to more accurate targeting and personalized marketing strategies. Enhancing Predictive Maintenance in Manufacturing The manufacturing sector also benefits immensely from data cleaning and data preprocessing. For instance, machine learning models predict equipment failures in predictive maintenance. However, the sensor data collected can be noisy and contain outliers. One can improve the data quality by applying data cleaning techniques to remove these outliers and fill in missing values. Further, preprocessing steps like feature scaling can help create more accurate predictive models, reducing downtime and saving costs. Streamlining Fraud Detection in Finance Data cleaning and preprocessing are crucial for fraud detection in the financial sector. Financial transaction data is often large and complex, with many variables. Cleaning this data by handling missing values and inconsistencies, and preprocessing it through techniques like feature selection, can significantly improve the performance of machine learning models for detecting fraudulent transactions. These examples highlight the transformative power of data cleaning and data preprocessing in various industries. By ensuring data quality and preparing it for machine learning models, these processes can lead to more accurate predictions and better decision-making. Data Cleaning & Data Preprocessing: Key Takeaways Data cleaning and preprocessing are foundational steps ensuring our models' reliability and accuracy, safeguarding them from misleading data and inaccurate predictions. This comprehensive guide explored various data cleaning and preprocessing techniques and tools—the importance of these processes and how they impact the overall data science pipeline. We've explored techniques like handling missing values, removing duplicates, data scaling, and feature selection, each crucial role in preparing your data for machine learning models. We've also delved into the realm of tools and libraries that aid in these processes, such as the versatile Pandas library and the specialized DataHeroes library. When used effectively, these tools can significantly streamline data cleaning and preprocessing tasks. Remember that every dataset is unique, with its challenges and requirements. Therefore, the real test of your data cleaning skills lies in applying these techniques to your projects, tweaking and adjusting as necessary to suit your needs.

Aug 09 2023

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How To Detect Data Drift on Datasets

Ensuring the accuracy and reliability of machine learning models is crucial in today’s ever-evolving world. However, the data upon which we rely is rarely static and can change in unpredictable ways over time. This phenomenon is known as data drift, and it poses a significant challenge to the effectiveness of models. In this article, you will learn about the concept of data drift, why models drift, and effective methods for detecting drift.  What is Data Drift? Data drift, also known as covariate shift, occurs when the statistical properties of the input data change over time, resulting in a discrepancy between the distribution of the data used during model training and the distribution of data encountered during model deployment or in real-world scenarios. Put simply, data drift means that the data on which a model was built is no longer representative of the data it is expected to make predictions on. Data drift can significantly impact the performance and accuracy of machine learning models. When the underlying data distribution changes, the model's assumptions become invalid, leading to suboptimal predictions and potentially inaccurate results. For instance, a model trained to predict customer preferences based on historical data may fail to capture changing trends or external events, resulting in decreased predictive power. Don’t let your model’s quality drift away Concept drift occurs when the relationship between input features and the target variable changes over time. As a result, the model's original assumptions are outdated. To address data drift and concept drift, it is important to continuously monitor the model's performance and update it with new data while employing techniques that are robust to drift. This helps maintain the model's accuracy and adaptability in dynamic data environments. It is crucial for data scientists and practitioners to stay vigilant against concept drift and data drift to ensure their models remain reliable and effective in ever-changing data landscapes. Why do Models Drift? Automatic Learning to Detect Concept Drift Models drift primarily due to changes in the underlying data distribution. There are several factors that can contribute to data drift and ultimately lead to model drift: Changing User Behavior As societies, markets, and industries evolve, so do people’s behaviors and preferences. These changes can be caused by cultural shifts, technological advancements, or changing societal norms. For instance, consider an e-commerce website that sells fashion products. As new trends emerge and consumer preferences shift, the data generated by customer interactions will change as well. This can cause a shift in the distribution of purchase patterns, which can potentially affect the model’s effectiveness. Seasonal Variations Seasonal trends are common in many industries, with certain periods of the year showing distinct patterns of behavior. For instance, retail sales often surge during the holiday season. If a model is trained using data from a specific season and then deployed in a different season, the data distribution during deployment may differ significantly from what the model learned during training. As a result, the model's predictions may become less accurate. Instrumentation Change Changes in data collection methods and tools can cause variations in captured data, leading to shifts in the data distribution. If the model is not updated to account for these changes, it may experience drift. External Events External events like economic fluctuations, policy changes, or global crises, can have a big impact on data patterns. For instance, during an economic recession, consumer spending behavior can change drastically. These changes can cause significant data drift, which can affect the model's ability to make accurate predictions. Data Source Changes In many real-world scenarios, data is collected from multiple sources, each with its own unique characteristics and biases. As these sources evolve or new ones are added, the overall distribution of the dataset may change, causing data drift. Data Preprocessing Changes Data preprocessing is essential in preparing the data for model training. Changes in preprocessing techniques, such as feature scaling, encoding, or data imputation, can change the data distribution and affect the model's performance. If these changes are not taken into account, it can lead to data drift.  Data Quality Issues It is crucial to have high-quality data for model training and deployment. Poor data quality, such as missing values or outliers, can negatively affect the model's training process. If the quality of new data differs significantly from the training data, it can introduce biases and drift in the model's predictions. Read How to Choose the Right Data for Your Computer Vision Project for some valuable insights.   How to Detect Data Drift Detecting data drift is crucial to maintaining the effectiveness of machine learning models and ensuring the accuracy of data-driven insights. Let's take a look at how the following methods can be used to detect data drift. Data Quality Monitoring Data quality monitoring involves tracking the quality and characteristics of the data over time. This can help detect data drift in the following ways:  Summary Statistics: Monitor summary statistics (mean, variance, median, etc.) of important features in the dataset. If these statistics suddenly or gradually change, it could indicate potential data drift. Data Distribution: Track the distribution of features in the dataset. If the shape of the distribution changes significantly, it could be a sign of data drift.  Outlier Detection: Detect outliers in the data, as the emergence of new outliers can signal a shift in the data distribution. Missing Values: Analyze patterns of missing values in the data since data drift may lead to different patterns of missingness. Model Quality Monitoring Model quality monitoring involves assessing the behavior and performance of machine learning models over time. This involves several techniques such as: Prediction Errors: Monitor prediction errors on a validation or test dataset. If the errors increase, it could indicate data drift. Performance Metrics: Keep track of various performance metrics (accuracy, precision, recall, etc.) to detect changes in model performance. Confusion Matrix: Analyze the confusion matrix to check. If the patterns of misclassifications have changed, it could indicate data drift. Statistical Tests: Use statistical tests to compare model outputs for different time periods. If there are significant differences, it may be due to data drift. Data Versioning Data versioning is a useful way to detect changes in data over time. This involves keeping track of different versions of the dataset, which allows you to compare and analyze changes in data distributions and patterns between different snapshots. By comparing data from different versions, you can identify potential data drift and take appropriate actions to maintain model accuracy and reliability. Feedback Loops Feedback loops are essential to detecting data drift in machine learning models. These loops involve collecting new data and using it to evaluate the model's performance and identify potential drift issues. By continuously incorporating, feedback loops help data scientists stay vigilant against data drift and ensure the model's accuracy and reliability in evolving data environments. Feedback loops can help detect data drift in the following ways: Active Learning: New insights gained from the feedback loop can be used to implement active learning strategies. This allows the model to learn from the new data and update its knowledge to handle data drift effectively. Real-Time Monitoring: Feedback loops enable the collection of new data in real-time or at regular intervals. This fresh data can be compared to the existing dataset to detect any changes in data distribution. Human-in-the-loop: Use human feedback to validate model predictions and identify potential data drift issues. Read Human-in-the-Loop Machine Learning (HITL) Explained to learn more about active learning.   Data Drift Detection Methods KS test (Kolmogorov-Smirnov Test) The KS test is a statistical test commonly used to compare the distributions of two datasets. It measures the maximum difference between the cumulative distribution functions (CDFs) of the two datasets being compared. The test determines whether the two datasets come from the same underlying distribution. If the datasets are from the same distribution, the KS test yields a small p-value. If the p-value is significant, it indicates that the two datasets have different distributions, indicating potential data drift. Illustration of the Kolmogorov–Smirnov statistic. The red line is a model (Cumulative Distribution Function), the blue line is an empirical CDF, and the black arrow is the KS statistic. The KS test can be used to compare the distribution of key features in the training dataset with the new data collected for model validation or deployment. If the KS test detects a significant difference in the distributions, it means that the model may encounter data it was not trained on, and highlight the presence of data drift. Population Stability Index (PSI) The Population Stability Index (PSI) is a technique used widely in business applications to detect data drift. It measures the difference between the expected distribution, often based on historical data and the actual distribution of a dataset. PSI is usually calculated by dividing the data into bins or segments and comparing the frequency or density distribution of features between two datasets. Population Stability Index A high PSI value suggests that there has been a significant change in the distribution of a feature between two datasets. This might indicate data drift, which would prompt data scientists to investigate and take corrective measures, such as retraining the model with the latest data. Page Hinkley method The Page-Hinkley method is a sequential monitoring technique used to detect abrupt changes in data distribution. This is done by continuously comparing the observed data with the expected data distribution and accumulating a score based on the differences. If the score exceeds a predefined threshold, it signals potential data drift. The Page-Hinkley method is particularly useful for quickly responding to data drift.. You can detect and address data drift in real-time by continuously monitoring and comparing data to the baseline, ensuring that the model remains up-to-date with the changing data patterns. Hypothesis Test Hypothesis testing is a versatile method that can help identify data drift. To begin, you formulate a hypothesis about the data (e.g. the mean of a specific feature in the new data is the same as in the training data) and then test it using statistical methods. Statistical tests like t-tests are used to compare the means of the two datasets. If the test yields a significant difference, it suggests that the hypothesis is invalid and that data drift may be present. Hypothesis testing can also be used to compare various statistical measures between datasets and identify potential shifts in data distributions. By combining these methods, you can establish a comprehensive data drift detection framework that ensures the long-term accuracy and reliability of machine learning models. Regular monitoring, continuous evaluation, and adaptive strategies are essential components of a proactive approach to tackling data drift effectively. Detecting Data Drift using Encord Active Encord Active is an open-source active learning toolkit that not only enhances model performance but also detects data drift in machine learning models. Active learning works on the principle of iteratively selecting the most informative or uncertain data points for labeling by a human annotator, allowing the model to actively seek feedback from the annotation instances that are challenging to predict accurately. By combining active learning with data drift detection, the Encord Active toolkit offers an efficient and intelligent means to adapt machine learning models to evolving data distributions. Read A Practical Guide to Active Learning for Computer Vision for more insight on active learning.   Here's how to utilize Encord Active for data drift detection: Monitoring Data Distribution With Encord Active's visualization tool, you can analyze the distribution of incoming data. By comparing the distribution of new data with the original training data, you can detect any shifts or variations in data patterns. Data Quality Metrics The data quality metrics can be used to assess new data samples and compare them to the original dataset. If there are any deviations in data quality, such as labeling mistakes or discrepancies, this can indicate data drift. Data Quality page on Encord Active Model Evaluation With Encord Active, you can assess your model's performance on new data samples. If there is a decrease in model accuracy or other performance metrics, it might indicate data drift issues. Model Quality metric in Encord Active Active Learning and Labeling Encord Active uses active learning techniques to allow you to selectively label and prioritize high-value data samples for re-labeling. This process ensures that the model is regularly updated with relevant data, reducing the impact of data drift.   Data Drift: Key Takeaways Data drift occurs when the statistical properties of input data change over time, leading to discrepancies between training and real-world data. Data drift can significantly impact model performance and accuracy by invalidating model assumptions. Factors that contribute to data drift include changes in user behavior, seasonal variations, changes to the data source, and data quality issues. Detecting data drift is crucial for maintaining model effectiveness. Methods include data quality and model performance monitoring, data versioning, and feedback loops. Encord Active's features can help detect data drift by analyzing data distribution, using data quality metrics, model evaluation, and active learning techniques.

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An Introduction to Diffusion Models for Machine Learning

Machine learning and artificial intelligence algorithms are constantly evolving to solve complex problems and enhance our understanding of data. One interesting group of models is diffusion models, which have gained significant attention for their ability to capture and simulate complex processes like data generation and image synthesis. In this article, we will explore: What is diffusion? What are diffusion models? How do diffusion models work? Applications of diffusion models Popular diffusion models for image generation What is Diffusion? Diffusion is a fundamental natural phenomenon observed in various systems, including physics, chemistry, and biology.  This is readily noticeable in everyday life. Consider the example of spraying perfume. Initially, the perfume molecules are densely concentrated near the point of spraying. As time passes, the molecules disperse.  Diffusion is the process of particles, information, or energy moving from an area of high concentration to an area of lower concentration. This happens because systems tend to reach equilibrium, where concentrations become uniform throughout the system. In machine learning and data generation, diffusion refers to a specific approach for generating data using a stochastic process similar to a Markov chain. In this context, diffusion models create new data samples by starting with simple, easily generated data and gradually transforming it into more complex and realistic data. What are Diffusion Models in Machine Learning? Diffusion models are generative, meaning they generate new data based on the data they are trained on.  For example, a diffusion model trained on a collection of human faces can generate new and realistic human faces with various features and expressions, even if those specific faces were not present in the original training dataset. These models focus on modeling the step-by-step evolution of data distribution from a simple starting point to a more complex distribution. The underlying concept of diffusion models is to transform a simple and easily samplable distribution, typically a Gaussian distribution, into a more complex data distribution of interest through a series of invertible operations. Once the model learns the transformation process, it can generate new samples by starting from a point in the simple distribution and gradually "diffusing" it to the desired complex data distribution. Denoising Diffusion Probabilistic Models (DDPMs) DDPMs are a type of diffusion model used for probabilistic data generation. As mentioned earlier, diffusion models generate data by applying transformations to random noise. DDPMs, in particular, operate by simulating a diffusion process that transforms noisy data into clean data samples. Training DDPMs entails acquiring knowledge of the diffusion process’s parameters, effectively capturing the relationship between clean and noisy data during each transformation step. During inference (generation), DDPMs start with noisy data (e.g., noisy images) and iteratively apply the learned transformations in reverse to obtain denoised and realistic data samples. Diffusion Models: A Comprehensive Survey of Methods and Applications DDPMs are particularly effective for image-denoising tasks. They can effectively remove noise from corrupted images and produce visually appealing denoised versions. Moreover, DDPMs can also be used for image inpainting and super-resolution, among other applications. Score-Based Generative Models (SGMs) Score-Based Generative Models are a class of generative models that use the score function to estimate the likelihood of data samples. The score function, also known as the gradient of the log-likelihood with respect to the data, provides essential information about the local structure of the data distribution. SGMs use the score function to estimate the data's probability density at any given point. This allows them to effectively model complex and high-dimensional data distributions. Although the score function can be computed analytically for some probability distributions, it is often estimated using automatic differentiation and neural networks. Score-Based Generative Modeling with Critically-Damped Langevin Diffusion Using the score function, SGMs can generate data samples that resemble the training data distribution. Iteratively updating them toward the log-likelihoods negative gradient achieves this. Stochastic Differential Equations (Score SDEs) Stochastic Differential Equations (SDEs) are mathematical equations describing how a system changes over time when subject to deterministic and random forces. In generative modeling, Score SDEs can parameterize the score-based models. In Score SDEs, the score function is a solution to a stochastic differential equation. The model can learn a data-driven score function that adapts to the data distribution by solving this differential equation. In essence, Score SDEs use stochastic processes to model the evolution of data samples and guide the generative process toward generating high-quality data samples. Solving a reverse-time SDE yields a score-based generative model. Score-Based Generative Modeling through Stochastic Differential Equations Score SDEs and score-based modeling can be combined to create powerful generative models capable of handling complex data distributions and generating diverse and realistic samples. How do Diffusion Models Work? Diffusion models are generative models that simulate data generation using the "reverse diffusion" concept. Let's break down how diffusion models work step-by-step: Data Preprocessing The initial step involves preprocessing the data to ensure proper scaling and centering. Typically, standardization is applied to convert the data into a distribution with a mean of zero and a variance of one. This prepares the data for subsequent transformations during the diffusion process, enabling the diffusion models to effectively handle noisy images and generate high-quality samples. Forward Diffusion During forward diffusion, the model starts with a sample from a simple distribution, typically a Gaussian distribution, and applies a sequence of invertible transformations to "diffuse" the sample step-by-step until it reaches the desired complex data points distribution. Each diffusion step introduces more complexity to the data, capturing the intricate patterns and details of the original distribution. This process can be thought of as gradually adding Gaussian noise to the initial sample, generating diverse and realistic samples as the diffusion process unfolds. Training the Model Training a diffusion model involves learning the parameters of the invertible transformations and other model components. This process typically involves optimizing a loss function, which evaluates how effectively the model can transform samples from a simple distribution into ones that closely resemble the complex data distribution.  Diffusion models are often called score-based models because they learn by estimating the score function (gradient of the log-likelihood) of the data distribution with respect to the input data points. The training process can be computationally intensive, but advances in optimization algorithms, and hardware acceleration have made it feasible to train diffusion models on various datasets. Reverse Diffusion Once the forward diffusion process generates a sample from the complex data distribution, the reverse diffusion process maps it back to the simple distribution through a sequence of inverse transformations. Through this reverse diffusion process, diffusion models can generate new data samples by starting from a point in the simple distribution and diffusing it step-by-step to the desired complex data distribution. The generated samples resemble the original data distribution, making diffusion models a powerful tool for image synthesis, data completion, and denoising tasks. Benefits of Using Diffusion Models Diffusion models offer advantages over traditional generative models like GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). These benefits stem from their unique approach to data generation and reverse diffusion.  Image Quality and Coherence Diffusion models are adept at generating high-quality images with fine details and realistic textures. When reverse diffusion is used to examine the underlying complexity of the data distribution, diffusion models make images that are more coherent and have fewer artifacts than traditional generative models. OpenAI's paper, Diffusion Models Beat GANs on Image Synthesis shows that diffusion models can achieve image sample quality superior to current state-of-the-art generative models. Stable Training Training diffusion models are generally more stable than training GANs, which are notoriously challenging. GANs require balancing the learning rates of the generator and discriminator networks, and mode collapse can occur when the generator fails to capture all aspects of the data distribution. In contrast, diffusion models use likelihood-based training, which tends to be more stable and avoids mode collapse. Privacy-Preserving Data Generation Diffusion models are suitable for applications in which data privacy is a concern. Since the model is based on invertible transformations, it is possible to generate synthetic data samples without exposing the underlying private information of the original data.  Handling Missing Data Diffusion models can handle missing data during the generation process. Since reverse diffusion can work with incomplete data samples, the model can generate coherent samples even when parts of the input data are missing. Robustness to Overfitting Traditional generative models like GANs can be prone to overfitting, in which the model memorizes the training data and fails to generalize well to unseen data. Because they use likelihood-based training and the way reverse diffusion works, diffusion models are better at handling overfitting. This is because they create samples that are more consistent and varied. Interpretable Latent Space Diffusion models often have a more interpretable latent space than GANs. The model can capture additional variations and generate diverse samples by introducing a latent variable into the reverse diffusion process. The reverse diffusion process turns the complicated data distribution into a simple distribution. This lets the latent space show the data's important features, patterns, and latent variables. This interpretability, coupled with the flexibility of the latent variable, can be valuable for understanding the learned representations, gaining insights into the data, and enabling fine-grained control over image generation. Scalability to High-Dimensional Data Diffusion models have demonstrated promising scalability to high-dimensional data, such as images with large resolutions. The step-by-step diffusion process allows the model to efficiently generate complex data distributions without being overwhelmed by the data's high dimensionality. Applications of Diffusion Models Diffusion models have shown promise in various applications across domains due to their ability to model complex data distributions and generate high-quality samples. Let’s dive into some notable applications of diffusion models: Text to Video Make-A-Video: Text-to-Video Generation without Text-Video Data. Diffusion models are a promising approach for text-to-video synthesis. The process involves first representing the textual descriptions and video data in a suitable format, such as word embeddings or transformer-based language models for text and video frames in a sequence format. During the forward diffusion process, the model takes the encoded text representation and gradually generates video frames step-by-step, incorporating the semantics and dynamics of the text. Each diffusion step refines the rendered frames, transforming them from random noise into visually meaningful content that aligns with the text. The reverse diffusion process then maps the generated video frames back to the simple distribution, completing the text-to-video synthesis. This conditional generation enables diffusion models to create visually compelling videos based on textual prompts. It has potential applications in video captioning, storytelling, and creative content generation. However, challenges remain, including ensuring temporal coherence between frames, handling long-range dependencies in text, and improving scalability for complex video sequences. Meta's Make-A-Video is a well-known example of leveraging diffusion models to develop machine learning models for text-to-video synthesis. Image to Image Diffusion models offer a powerful approach for image-to-image translation tasks, which involve transforming images from one domain to another while preserving semantic information and visual coherence. The process involves conditioning the diffusion model on a source image and using reverse diffusion to generate a corresponding target image representing a transformed source version. To achieve this, the source and target images are represented in a suitable format for the model, such as pixel values or embeddings. During the forward diffusion process, the model gradually transforms the source image, capturing the desired changes or attributes specified by the target domain. This often involves upsampling the source image to match the resolution of the target domain and refining the generated image step-by-step to produce high-quality and coherent results. The reverse diffusion process then maps the generated target image back to the simple distribution, completing the image-to-image translation. This conditional generation allows diffusion models to excel in tasks like image colorization, style transfer, and image-to-sketch conversion.  The paper Denoising Diffusion Probabilistic Models (DDPM), which was initialized by Sohl-Dickstein et al. and then proposed by Ho et al. 2020 is an influential paper that showcases diffusion models as a potent neural network-based method for image generation tasks. Image Search Diffusion models are powerful content-based image retrieval techniques that can be applied to image search tasks. Using the reverse diffusion process, the first step in using diffusion models for image search is to encode the images in a latent space. During reverse diffusion, the model maps each image to a point in the simple distribution. This latent representation retains the essential visual information of the image while discarding irrelevant noise and details, making it suitable for efficient and effective similarity searches. When a query image is given for image search, the model encodes the query image into the same latent space using the reverse diffusion process. The similarity between the query and database images can be measured using standard distance metrics (e.g., Euclidean distance) in the latent space. Images with the most similar latent representations are retrieved, producing relevant and visually similar images to the query. This application of diffusion models for image search enables accurate and fast content-based retrieval, which is useful in various domains such as ai-generated logo templates, image libraries, image databases, and reverse image search engines. Diffusion models are one such model that powers the semantic search feature within Encord Active. When you log into Encord → Active → Choose a Project → Use the Natural Language or Image Similarity Search feature. Here is a way to search with image similarity as the query image: Image Similarity Search within Encord Active. Read the full guide, ‘How to Use Semantic Search to Curate Images of Products with Encord Active,' in this blog post. Reverse Image Search Diffusion models can be harnessed for reverse image search, also known as content-based image retrieval, to find the source or visually similar images based on a given query image.  To facilitate reverse image search with diffusion models, a database of images needs to be preprocessed by encoding each image into a latent space using the reverse diffusion process. This latent representation captures each image's essential visual characteristics, allowing for efficient and accurate retrieval. When a query image is provided for reverse image search, the model encodes it into the same latent space using reverse diffusion. By measuring the similarity between the query image's latent representation and the database images' latent representations using distance metrics (e.g., Euclidean distance), the model can identify and retrieve the most visually similar images from the database. This application of diffusion models for reverse image search facilitates fast and reliable content-based retrieval, making it valuable for various applications, including image recognition, plagiarism detection, and multimedia databases.  Well-known Diffusion Models for Image Generation Stable Diffusion High-Resolution Image Synthesis with Latent Diffusion Models Stable diffusion is a popular approach for image generation that uses diffusion models (DMs) and the efficiency of latent space representation. The method introduces a two-stage training process to enable high-quality image synthesis while overcoming the computational challenges associated directly operating in pixel space. In the first stage, an autoencoder is trained to compress the image data into a lower-dimensional latent space that maintains perceptual equivalence with the original data. This learned latent space is an efficient and scalable alternative to the pixel space, providing better spatial dimensionality scaling properties. By training diffusion models in this latent space, known as Latent Diffusion Models (LDMs), Stable Diffusion achieves a near-optimal balance between complexity reduction and detail preservation, significantly boosting visual fidelity. High-Resolution Image Synthesis with Latent Diffusion Models Stable diffusion introduces cross-attention layers into the model architecture, enabling the diffusion models to become robust and flexible generators for various conditioning inputs, such as text or bounding boxes. This architectural enhancement opens up new possibilities for image synthesis and allows for high-resolution generation in a convolutional manner. The approach of stable diffusion has demonstrated remarkable success in image inpainting, class-conditional image synthesis, text-to-image synthesis, unconditional image generation, and super-resolution tasks. Moreover, it achieves state-of-the-art results while considerably reducing the computational requirements compared to traditional pixel-based diffusion models. The code for stable diffusion has been made publicly available on GitHub. DALL-E 2 Hierarchical Text-Conditional Image Generation with CLIP Latents DALL-E 2 utilizes contrastive models like CLIP to learn robust image representations that capture semantics and style. It has a 2-stage model consisting of a prior stage that generates a CLIP image embedding based on a given text caption and a decoder stage. The model's decoders use diffusion. These models are conditioned on image representations and produce variations of an image that preserve its semantics and style while altering non-essential details. Hierarchical Text-Conditional Image Generation with CLIP Latents The CLIP joint embedding space allows language-guided image manipulations to happen in a zero-shot way. This allows the diffusion model to create images based on textual descriptions without direct supervision. 🔥 NEW RELEASE: We released TTI-Eval (text-to-image evaluation), an open-source library for evaluating zero-shot classification models like CLIP and domain-specific ones like BioCLIP against your (or HF) datasets to estimate how well the model will perform. Get started with it on GitHub, and do ⭐️ the repo if it's awesome. 🔥 Imagen Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Imagen is a text-to-image diffusion model that stands out for its exceptional image generation capabilities. The model is built upon two key components: large pre-trained frozen text encoders and diffusion models. Leveraging the strength of transformer-based language models, such as T5, Imagen showcases remarkable proficiency in understanding textual descriptions and effectively encoding them for image synthesis. Imagen uses a new thresholding sampler, which enables the use of very large classifier-free guidance weights. This enhancement further enhances guidance and control over image generation, improving photorealism and image-text alignment. Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding The researchers introduce a novel, Efficient U-Net architecture to address computational efficiency. This architecture is optimized for better computing and memory efficiency, leading to faster convergence during training. U-Net: Convolutional Networks for Biomedical Image Segmentation A significant research finding is the importance of scaling the pre-trained text encoder size for the image generation task. Increasing the size of the language model in Imagen substantially positively impacts both the fidelity of generated samples and the alignment between images and corresponding text descriptions. This highlights the effectiveness of large language models (LLMs) in encoding meaningful representations of text, which significantly influences the quality of the generated images. The PyTorch implementation of Imagen can be found on GitHub. GLIDE Guided Language to Image Diffusion for Generation and Editing (GLIDE) is another powerful text-conditional image synthesis model by OpenAI. It is a computer vision model based on diffusion models. GLIDE leverages a 3.5 billion-parameter diffusion model with a text encoder to condition natural language descriptions. The primary goal of GLIDE is to generate high-quality images based on textual prompts while offering editing capabilities to improve model samples for complex prompts. GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models In the context of text-to-image synthesis, GLIDE explores two different guidance strategies: CLIP guidance and classifier-free guidance. Through human and automated evaluations, the researchers discovered that classifier-free guidance yields higher-quality images than the alternative. This guidance mechanism allows GLIDE to generate photorealistic samples that closely align with the text descriptions. One notable application of GLIDE in computer vision is its potential to significantly reduce the effort required to create disinformation or Deepfakes. However, to address ethical concerns and safeguard against potential misuse, the researchers have released a smaller diffusion model and a noisy CLIP model trained on filtered datasets.  OpenAI has made the codebase for the small, filtered data GLIDE model publicly available on GitHub. Diffusion Models: Key Takeaways Diffusion models are generative models that simulate how data is made by using a series of invertible operations to change a simple starting distribution into the desired complex distribution. Compared to traditional generative models, diffusion models have better image quality, interpretable latent space, and robustness to overfitting. Diffusion models have diverse applications across several domains, such as text-to-video synthesis, image-to-image translation, image search, and reverse image search. Diffusion models excel at generating realistic and coherent content based on textual prompts and efficiently handling image transformations and retrievals. Popular models include Stable Diffusion, DALL-E 2, and Imagen.

Aug 08 2023

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How To Mitigate Bias in Machine Learning Models

Machine learning has revolutionized many industries by automating decision-making processes and improving efficiency. In recent years, data science, machine learning, and artificial intelligence have become increasingly prevalent in various applications, transforming industries and everyday life. As these technologies become more integrated into the real world, one of the significant challenges in responsibly deploying machine learning models is mitigating bias. To ensure that AI systems are fair, reliable, and equitable, addressing bias is crucial. In this article, you will learn about:  Types of bias Impacts of bias Evaluating bias in ML models Mitigating bias in ML models Mitigating bias with Encord Active Key takeaways Types of Bias Bias in machine learning refers to systematic errors introduced by algorithms or training data that lead to unfair or disproportionate predictions for specific groups or individuals. Such biases can arise due to historical imbalances in the training data, algorithm design, or data collection process. If left unchecked, biased AI models can perpetuate societal inequalities and cause real-world harm. Biases can be further categorized into explicit and implicit bias: Explicit bias refers to conscious prejudice held by individuals or groups based on stereotypes or beliefs about specific racial or ethnic groups. For example, an AI-powered customer support chatbot may be programmed with bias towards promoting products from manufacturers that offer higher commissions or incentives to the company. Implicit bias is a type of prejudice that people hold or express unintentionally and outside of their conscious control. These biases can have an unconscious impact on perceptions, assessments, and choices because they are often deeply ingrained in societal norms. The collection of data, the creation of algorithms, and the training of models can all unintentionally reflect unconscious bias. For simplicity, we can categorize data bias into three buckets: data bias, algorithm bias, and user interaction bias. These categories are not mutually exclusive and are often intertwined. A Survey on Bias and Fairness in Machine Learning Bias in Data When biased data is used to train ML algorithms, the outcomes of these models are likely to be biased as well. Let’s look at the types of biases in data: Measurement bias occurs when the methods used to record data systematically deviate from the true values, resulting in consistent overestimation or underestimation. This distortion can arise due to equipment calibration errors, human subjectivity, or flawed procedures. Minor measurement biases can be managed with statistical adjustments, but significant biases can compromise data accuracy and skew outcomes, requiring cautious validation and re-calibration procedures. Omitted variable bias arises when pertinent variables that impact the relationship between the variables of interest are left out from the analysis. This can lead to spurious correlations or masked causations, misdirecting interpretations and decisions. Including relevant variables is vital to avoid inaccurate conclusions and to capture the delicate interactions that influence outcomes accurately. Aggregation bias occurs when data is combined at a higher level than necessary, masking underlying variations and patterns within subgroups. This can hide important insights and lead to inaccurate conclusions.  Avoiding aggregation bias involves striking a balance between granularity and clarity, ensuring that insights drawn from aggregated data reflect the diversity of underlying elements. Sampling bias occurs when proper randomization is not used for data collection. Sampling bias can help optimize data collection and model training by focusing on relevant subsets, but may introduce inaccuracies in representing the entire population. Linking bias occurs when connections between variables are assumed without solid evidence or due to preconceived notions. This can lead to misguided conclusions. It's important to establish causal relationships through rigorous analysis and empirical evidence to ensure the reliability and validity of research findings. Labeling bias occurs when the data used to train and improve the model performance is labeled with incorrect or biased labels. Human annotators may inadvertently introduce their own biases when labeling data, which can be absorbed by the model during training. For a deep dive into datasets, read Introduction to Balanced and Imbalanced Datasets in Machine Learning.   Bias in Algorithms Bias in algorithms reveals the subtle influences of our society that sneak into technology. Algorithms are not inherently impartial; they can unknowingly carry biases from the data they learn from. It's important to recognize and address this bias to ensure that our digital tools are fair and inclusive. Let’s look at the types of biases in algorithms: Algorithmic bias emerges from the design and decision-making process of the machine learning algorithm itself. Biased algorithms may favor certain groups or make unfair decisions, even if the training data is unbiased. User interaction bias can be introduced into the model through user interactions or feedback. If users exhibit biased behavior or provide biased feedback, the AI system might unintentionally learn and reinforce those biases in its responses. Popularity bias occurs when the AI favors popular options and disregards potentially superior alternatives that are less well-known. Emergent bias happens when the AI learns new biases while working and makes unfair decisions based on those biases, even if the data it processes is not inherently biased. Evaluation bias arises when we use biased criteria to measure the performance of AI. This can result in unfair outcomes and lead us to miss actual issues. Bias in Machine Learning - What is it Good for? Bias in User Interaction User biases can be introduced into the model through user interactions or feedback. If users exhibit biased behavior or provide biased feedback, the AI system might unintentionally learn and reinforce those biases in its responses. Historical bias is inherited from past embedded social or cultural inequalities. When historical data contains biases, AI models can perpetuate and even amplify these biases in their predictions. Population bias occurs when the AI prioritizes one group over others due to the data it has learned from. This can result in inaccurate predictions for underrepresented groups. Social bias can arise from cultural attitudes and prejudices in data, leading the AI to make biased predictions that reflect unfair societal views. Temporal bias occurs when the AI makes predictions that are only true for a certain time. Without considering changes over time, the results will be outdated. While this list of biases is extensive, it is not exhaustive. Wikipedia compiled a list of cognitive biases with over 100 types of human biases.    Impacts of Bias The impact of bias in machine learning can be far-reaching, affecting various domains:  Healthcare Training AI systems for healthcare with biased data can lead to misdiagnosis or unequal access to medical resources for different demographic groups. These biases in AI models can disproportionately affect minority communities, leading to inequitable healthcare outcomes. If the training data mostly represents specific demographic groups, the model may struggle to accurately diagnose or treat conditions in underrepresented populations. Criminal Justice Bias in predictive algorithms used in criminal justice systems for risk assessment can perpetuate racial disparities and lead to biased sentencing. For example, biased recidivism prediction models may unfairly label certain individuals as high-risk, resulting in harsher treatment or longer sentences.  A Survey on Bias and Fairness in Machine Learning looked into judges' use of COMPAS to determine whether to release an offender in prison. An investigation into the software revealed that it had a bias against African-Americans.   Employment and Hiring Biases in AI-powered hiring systems can perpetuate discriminatory hiring practices, hindering opportunities for minorities and reinforcing workforce inequalities. Finance Bias in AI-powered financial applications can result in discriminatory lending practices. AI models that use biased data to determine creditworthiness may deny loans or offer less favorable terms based on irrelevant features. Impact of Bias in Different Applications Evaluating Bias in ML Models Evaluating and quantifying bias in machine learning models is essential for effectively addressing this issue. There are several metrics and methodologies used to assess bias, including:  Disparate Impact Analysis This technique examines the disparate impact of an AI model's decisions on different demographic groups. It measures the difference in model outcome for various groups and highlights potential biases.  Disparate Impact Analysis is a vital tool for assessing the fairness of AI models. It helps detect potential discriminatory effects based on protected attributes like race or gender. By comparing model performance across different demographic groups, it reveals if certain groups are unfairly favored or disadvantaged. Addressing bias issues through data modification, algorithm adjustments, or decision-making improvements is essential for creating equitable results. Fairness Metrics Several fairness metrics have been proposed to quantify bias in machine learning models. Examples include Equal Opportunity Difference, Disparate Misclassification Rate, and Treatment Equality. These metrics help assess how fairly the model treats different groups. Post-hoc Analysis Post-hoc analysis involves examining an AI system’s decisions after deployment to identify instances of bias and understand its impact on users and society. One application of post-hoc analysis is in sentiment analysis for customer reviews. This allows companies to assess how well their model performs in classifying reviews as positive, negative, or neutral. This analysis is instrumental in natural language processing tasks like text classification using techniques such as RNN or BERT. Mitigating Bias in ML Models To reduce bias in machine learning models, technical, ethical, and organizational efforts must be combined. There are several strategies to mitigate bias, including: Diverse and Representative Data Collection It is essential to have diverse and representative training data to combat data bias. Data collection processes should be carefully designed to ensure a fair representation of all relevant data points. This may involve oversampling underrepresented groups or using advanced techniques to generate synthetic data. This technique helps improve model accuracy and reduce bias towards the majority class. Bias-Aware Algorithms To foster fairness in machine learning systems, developers can utilize fairness-aware algorithms that explicitly incorporate fairness constraints during model training. Techniques such as adversarial training, reweighing, and re-sampling can be employed to reduce algorithmic bias and ensure more equitable outcomes. Explainable AI and Model Interpretability Enhancing the interpretability of AI models can aid in identifying and addressing bias more effectively. By understanding the model's decision-making process, potential biases can be identified and appropriate corrective measures can be taken. Pre-processing and Post-processing Pre-processing techniques involve modifying the training data to reduce bias, while post-processing methods adjust model outputs to ensure fairness. These techniques can help balance predictions across different groups. Regular Auditing and Monitoring Regularly auditing and monitoring AI models can detect bias and ensure ongoing fairness. Feedback loops with users can also help identify and address potential user biases. Mitigating Bias with Encord Active Encord Active offers features to help reduce bias in datasets, allowing you to identify and address any potential biases in your data workflow. By leveraging the data points metadata filters, you can filter the dataset based on attributes like Object Class and Annotator.  These capabilities enable you to concentrate on specific classes or annotations created by particular annotators on your team. This helps ensure that the dataset is representative and balanced across various categories. You can identify and mitigate any inadvertent skew in the dataset by paying attention to potential biases related to annotators or object classes. Encord Active's user-defined tag filters are crucial for reducing bias. These filters allow you to apply custom tags to categorize data based on specific attributes, preferences, or characteristics. By leveraging these user-defined tags for filtering, data can be organized and assessed with respect to critical factors that influence model performance or decision-making processes. For example, if there are sensitive attributes like race or gender that need to be considered in the dataset, corresponding tags can be assigned to filter the data appropriately, ensuring fair representation and equitable outcomes. Combined with these filtering capabilities, Encord Active's ability to find outliers based on pre-defined and custom metrics also aids in bias reduction. Outliers, or data points that deviate significantly from the norm, can indicate potential biases in the dataset.  By identifying and labeling outliers for specific metrics, you can gain insights into potential sources of bias and take corrective measures. This process ensures that your dataset is more balanced, reliable, and representative of the real-world scenarios it aims to address. Ultimately, this can lead to more robust and fair machine learning models and decision-making processes. The Summary Tab of each Quality Metrics shows the outliers. Clicking on each outlier gives deeper insight into moderate and severe outliers. Bias in ML Models: Key Takeaways Bias in machine learning is a major challenge that requires attention and efforts from the AI community. Identifying sources of bias and evaluating its impact are essential steps toward creating fair and ethical AI systems. Effective mitigation strategies can help reduce bias and promote fairness in machine learning models. Encord Active and its quality metrics can be used to mitigate bias. Reducing bias in machine learning is an ongoing journey that requires a commitment to ethical considerations and fairness. The collaboration among data scientists, developers, organizations, and policymakers is crucial in ensuring that AI technologies benefit society without perpetuating biases or discrimination.

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Mask-RCNN vs. Personalized-SAM: Comparing Two Object Segmentation Models

Let's take a moment to talk about one of the coolest things in AI right now: object segmentation. Simply put, object segmentation is about making sense of a picture by splitting it into its core parts and figuring out what's what. It’s kind of like solving a jigsaw puzzle, but instead of fitting pieces together, our AI is identifying and labeling things like cats, cars, trees, you name it! The practical implications of successful object segmentation are profound, spanning diverse industries and applications - from autonomous vehicles distinguishing between pedestrians and road infrastructure to medical imaging software that identifies and isolates tumors in a patient's scan to surveillance systems that track individuals or objects of interest. Each of these cases hinges on the capability to segment and identify objects within a broader context precisely; as a result, precise detection provides a granular understanding that fuels informed decisions and actions. Object segmentation is the secret sauce that makes this possible. Yet, as powerful as object segmentation is, it comes with its challenges. Conventionally, segmentation models require vast quantities of annotated data to perform at an optimal level. The task of labeling and annotating images can be labor-intensive, time-consuming, and often requires subject matter expertise, which can be a barrier to entry for many projects. At Encord, we utilize micro-models to speed up the segmentation process of computer vision projects. Users only label a few images from their dataset, and Encord micro-models learn the visual patterns of these labels by training on them. The trained micro-model is used to label the unlabeled images in the dataset, thereby automating the annotation process. As new foundational vision models such as DINO, CLIP, and Segment Anything Model emerge, the performance of these micro-models also increases. So we have decided to put two object segmentation models to the test to see how well they handle few-shot learning. We challenge these models to generate predictions for unlabeled images based on training a Mask-RCNN with 10 images and a recently proposed Personalized-SAM with 1 image, pushing the boundaries of what few-shot learning can achieve.  Segment Anything Model (SAM) Personalized-SAM uses the Segment Anything Model (SAM) architecture under the hood. Let’s dive into how SAM works. The Segment Anything Model (SAM) is a new AI model from Meta AI that can "cut out" any object, in any image, with a provided input prompt. SAM is a promptable segmentation system with zero-shot generalization to unfamiliar objects and images, without the need for additional training. So basically, SAM takes two inputs, an image and a prompt, and outputs a mask that is in relation to the prompt. The below figure shows the main blocks of the SAM architecture: Personalize Segment Anything Model with One Shot The image is passed through an image encoder which is a vision transformer model; this block is also the largest block of all SAM architecture. The image encoder outputs a 2D embedding (this being 2D is important here, as we will see later in Personalized-SAM). The prompt encoder processes the prompt, which can be points, boxes, masks, and texts. Finally, 2D image embedding and prompt embedding is fed to the mask decoder, which will output the final predicted masks. As there is a visual hierarchy of objects in images, SAM can output three different masks for the provided prompt. For a deep dive into the Segment Anything Model, read Meta AI's New Breakthrough: Segment Anything Model (SAM) Explained There are two main drawbacks of the SAM architecture: For each new image, a human is needed for the prompting. The segmented region is agnostic to the class; therefore, the class should be specified for each generated mask. Now, let’s move on to the Personalized-SAM to see how these issues are addressed. Personalized-SAM (Per-SAM) Personalized SAM (Per-SAM) is a training-free Personalization approach for SAM. The proposed approach works by first localizing the target concept in the image using a location prior, and then using a combination of target-guided attention, target-semantic prompting, and cascaded post-refinement to segment the target concept. Then, it uses a combination of target-guided attention, target-semantic prompting, and cascaded post-refinement to segment the target concept.  Personalize Segment Anything Model with One Shot Learning the target embedding First, the user provides an image and mask of the target concept (object). Then the image is processed through the image encoder block of the SAM architecture. Remember what the image encoder’s output was? Yes, it’s a 2D embedding! Now, one needs to find where the mask of the target object corresponds to in this 2D embedding space, as these embeddings will be the representation of our target concept. Since the spatial resolution is decreased in 2D embeddings, the mask should also be downscaled to that size. Then, the average of the vectors at locations that correspond to the downscaled mask should be obtained. Finally, we have a fixed embedding (or representation) for the target concept, which we will utilize in the next step. Getting a similarity map to generate prompts Now it is time for the inference on a new image. Per-SAM first processes it through the SAM image encoder to obtain a 2D embedding. The next step is crucial: finding the similarity between each location in the 2D embedding, and the target embedding that we obtained in the first step. The authors of Per-SAM use Cosine distance to calculate the similarity map. Once the similarity map is obtained, points for prompts can be generated for the prompt decoder. The maximum similarity location is determined as the positive point, and the minimum similarity location is determined as the negative point. So, now we have a prompt for the desired class for the new image. Can you guess what is next? We just need to apply the classic SAM process to get the mask. The image and prompts are given to the model and a segmentation mask is generated.  In the Per-SAM paper, the authors also use the similarity map as a semantic prompt. Moreover, they consecutively obtain new prompts (bounding box and segmented region) from the generated mask and refine the final predicted mask, which improves the overall result. Fine-tuning the Per-SAM Personalize Segment Anything Model with One Shot  In most of the images, objects have a conceptual hierarchy, which is already discussed in the SAM paper; therefore, SAM outputs 3 different masks when the prompt is ambiguous (especially for the point prompts). The authors of the Per-SAM propose another light layer, consisting of only 2 weights, on top of the SAM architecture to weigh the proposed masks. The entire SAM is frozen, and the generated masks are weighted with the learnable parameters, which results in rapid  (under 10 seconds) training. The reported results show that when fine-tuning is applied, the final performance result is significantly improved. Per-SAM vs. Mask R-CNN: Comparison Mask R-CNN (Mask Region-based Convolutional Neural Network) is a flexible and powerful framework for object instance segmentation, which is also one of the many deep learning models in Encord Apollo. Unlike SAM architecture, Mask R-CNN only accepts images as input and class annotations as output. Although it emerged a long time ago, it is still a very powerful model and is widely used in lots of applications and research papers as a baseline model. Therefore, comparing the Per-SAM against Mask R-CNN would be a sensible choice. Benchmark Dataset: DeepFashion-MultiModal  In this benchmark, we will use the DeepFashion-MutiModal dataset, which is a comprehensive collection of images created to facilitate a variety of tasks within the field of clothing and fashion analysis. It contains 44,096 high-resolution human images, including 12,701 full-body human images. All full-body images are manually annotated for 24 classes. The dataset also has key points, dense pose and textual description annotations. An image and its mask annotations The dataset is uploaded to Encord Active and here are more sample images and their annotations as seen in Encord Active platform: First, we have created a training set (10 images) and a test (inference) set (300 images) on the Encord platform. Encord platform provides two important features for this benchmark: We can visualize the images and labels easily. We can import predictions to the Encord Active platform which calculates the model performance. We have trained a MaskRCNN model on the training set using Encord Apollo. For the Per-SAM architecture, we have only used the first image in the training set. For the sake of simplicity, only ‘top’ type of class is used for this evaluation. The trained Mask-RCNN model and Per-SAM models are compared on the test images. Here is the benchmark table: Experiment results indicate that training-free Per-SAM outperforms the Mask-RCNN model, which shows the strong capability and clever approach of this new foundational model. Mask-RCNN and Per-SAM-1 shot performances are quite low which shows the difficulty of the dataset. When Per-SAM is trained on the image to weigh the masks, its performance is significantly improved. With fine tuning, Per-SAM learns the concept of the target object which results in a dramatic decrease in false positives and an increase in true positives. As a final step, we applied a post-processing step that removes the very small objects (if the object area to total area is less than 0.1%) in Per-SAM predictions. Which significantly reduced the false positives and resulted in a boost in the mAP score. When we examine the individual image results, we see that Per-SAM performs fairly well on most objects. Here are the true positives, false positives and false negatives: True Positives False Positives False Negatives Conclusion The experimental results show that new foundational models are very powerful regarding few-shot learning capabilities. Since their learning capacity is much more advanced than previous architectures and they are trained with millions of images (SAM is trained with over 11 million images with more than 1 billion masks), they can quickly learn new classes from a few images.  While the introduction of SAM was a milestone for computer vision, Per-SAM shows that there are many areas to explore on top of the SAM that will open new and more effective solutions. In Encord, we are constantly exploring new research and integrating them into the Encord platform to ease the annotation processes of our users.

Aug 03 2023

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Top Tools for Outlier Detection in Computer Vision

Data contains hidden insights that completely alter how we make business decisions. However, data often consists of abnormal instances, known as outliers, that can distort the outcome of data processing and analysis. Moreover, machine learning (ML) models trained using data with outliers may have suboptimal predictive performance. Hence, outlier detection is a crucial step in any data pipeline.   Here's the catch: manually identifying data outliers is difficult and time-consuming, especially for large datasets. As a result, data scientists and artificial intelligence (AI) practitioners employ outlier detection tools to quickly identify outliers and streamline their data processing and ML pipelines. In this guide, we’ll explore outlier detection techniques and list the top tools that can be utilized for this purpose. These include: Encord Active Lightly Aquarium Voxel Deepchecks Arize Outlier Detection: Types & Methods Outliers are data points with extreme values that are at disproportionately large distances from the normal distribution of the dataset. They represent an abnormal pattern compared to the regular data points. They can occur for various reasons, including data entry and label errors, measurement discrepancies, missing values, and rare events. There are three main types of outliers: Global or Point Outliers: Individual data points that deviate significantly from the normal distribution of the dataset. Contextual Outliers: Data points with abnormal distances within a specific context or subset of the data.  Collective Outliers: Groups or subsets of data that exhibit unusual patterns compared to the entire dataset.  Outliers are also classified based on the number of variables. These are:  Univariate Outliers: Data points of a single variable that are distant from regular observations. Multivariate Outliers: A combination of extreme data values on two or more variables. Illustration of outliers in 2D data Now, let’s explore some common outlier detection methods that AI practitioners use: Z-score Method This method identifies outliers based on the number of standard deviations from the mean. In other words, the z-score is a statistical measurement that determines how distant a data point is from its distribution. Typically, a data point with a Z-score beyond +3 or -3 is considered an outlier. The Z-score results are best visualized with histograms and scatter plots. Clustering Method This method identifies various data clusters in the dataset distribution using techniques like:  K-means clustering, a technique that creates clusters of similar data points, where each cluster has a centroid (center points or cluster representatives within a dataset), and data points within one cluster are dissimilar to the data points in another cluster.  Density-based spatial clustering of applications with noise (DBSCAN) to detect data points that are in areas of low density (where the nearest clusters are far away)  In such methods, outliers are identified by calculating the distance between each data point and the centroid, and data points that are farthest from the cluster centers are typically categorized as outliers. The clustering results are best visualized on scatter plots. Interquartile range (IQR) Method  This method identifies outliers based on their position in relation to the data distribution's percentiles. The IQR is calculated as the difference between the third quartile (Q3) and first quartile (Q1) in a rank-ordered portion of data. Typically, an outlier is identified when a data point is more than 1.5 times the IQR distance from either the lower (Q1) or upper quartile (Q3). The IQR method results are best visualized with box plots. Many outlier detection tools use similar or more advanced methods to quickly find anomalies in large datasets. And there are many out there. How can you pick the one that best suits your requirements?  Let’s compare our curated list of top outlier detection tools to help you find the right one. Our comparison will be based on key factors, including outlier detection features, support for data types, customer support, and pricing. Encord Active Encord Active is a powerful active learning toolkit for advanced error analysis for computer vision data to accelerate model development. Encord Active dashboard Benefits & Key Features Surface and prioritize the most valuable data for labeling Search and curate data across images, videos, DICOM files, labels, and metadata using natural language search Auto-find and fix dataset biases and errors like outliers, duplication, and labeling mistakes Find machine learning model failure modes and edge cases Employs precomputed interquartile ranges to process visual data and uncover anomalies Integrated tagging for data and labels, including outlier tagging Export, re-label, augment, review, or delete outliers from your dataset Employs quality metrics (data, label, and model) to evaluate and improve ML pipeline performance across several dimensions, like data collection, data labeling, and model training.  Integrated filtering based on quality metrics Supports data types like jpg, png, tiff, and mp4 Supports label types like bounding boxes, polygons, segmentation, and classification Advanced Python SDK and API access to programmatically access projects, datasets, and labels Provides interactive visualizations, enabling users to analyze detected outliers comprehensively Offers collaborative workflows, enabling efficient teamwork and improved annotation quality Best for Teams Who Are looking to upgrade from in-house solutions and require a reliable, secure, and collaborative platform to scale their anomaly detection workflows effectively. Need a suite of powerful tools to work on complex computer vision use cases across verticals like smart cities, AR/VR, autonomous transportation, and sports analytics. Haven't found an anomaly detection platform that aligns perfectly with their specific use case requirements Read our step-by-step guide to Improving Training Data with Outlier Detection with Encord Pricing There are two core offerings: a free, open-source version, and a team plan which requires a support contact. Lightly Lightly is a data curation software for computer vision that offers improved model accuracy by utilizing active learning to find clusters or subsets of high-impact data within your training dataset. Lightly dashboard Benefits & Key Features Data selection is done via active and self-supervised learning algorithms based on three input types: embeddings, metadata, and predictions. Automates image and video data curation at scale to mitigate dataset bias Built-in capability to check for corrupt images or broken frames Data drift and model drift monitoring Python SDK to integrate with other frameworks and your existing ML stack using scripts LightlyWorker tool – a docker container to leverage GPU capabilities Best for Teams Who Require GPU capabilities to curate large-scale vision datasets, including special data types like LIDAR, RADAR, and medical. Want a collaborative platform for dataset sharing Pricing Lightly offers free community and paid versions for teams and custom plans. Aquarium Aquarium is an ML data operations platform that allows data management with a focus on improving training data. It utilizes embedding technology to surface problems in model performance.  Aquarium dashboard Users can upload streaming datasets into Aquarium's data operations platform. It retains the history of changes, enabling users to analyze the evolution of the dataset over time and gain insights.  Benefits & Key Features Generate, process, and query embeddings to find clusters of high-quality data from unlabeled datasets Allows for a variety of data to be curated, including images, 3D data, audio, and text Integrates with data labeling suppliers and ML tools like TensorFlow, Keras, Google Cloud, Azure, and AWS Inspects data and labels using visualization to find errors and bad data quickly Automatically analyze and calculate model metrics to identify erroneous data points Community and shared Slack channel support, as well as solution engineering assistance Best for Teams Who Require integration of vendor systems with a data operations platform enabling efficient data flow Need ML team collaboration on data curation and evaluation tasks Interested in learning more about the role of data operations? Read our comprehensive Best Practice Guide for Computer Vision Data Operations Teams.   Pricing Aquarium offers a free tier for a single user. They also offer team, business, and enterprise tiers for multiple users. Voxel51 Voxel51 is an open-source toolkit for curating high-quality datasets and building computer vision production workflows. FiftyOne dashboard Benefits & Key Features Integrates with ML tools to annotate, train, filter, and evaluate models Identifies your model’s failure modes Removes redundant images from training data Finds and corrects label mistakes to curate higher-quality datasets Dedicated slack channel for customer support Best for Teams Who Want to start with open-source tooling  Require a graphical user interface that enables them to visualize, browse, and interact directly with their datasets Pricing There are two core offerings: FiftyOne, a free, open-source platform, and FiftyOne Teams plan, which requires a support contact. Deepchecks Deepchecks is an ML platform and Python library for deep learning model monitoring and debugging. It offers validation of machine learning algorithms and data with minimal effort in the research and production phases. Deepchecks dashboard The Deepchecks tool utilizes the LoOP algorithm, a method for detecting outliers in a dataset across multiple variables by comparing the density in the area of a sample with the densities in the areas of its nearest neighbors.  Benefits & Key Features Utilizes Gower distance with LoOP algorithm to identify outliers Real-time monitoring of model performance and metrics (such as label drift) Provides Role-Based Access Control (RBAC) Prioritizes data privacy by encrypting data during transit and storage Slack community and Enterprise support for users Best for Teams Who Are required to monitor model performance and find and resolve production issues Deal with sensitive data and value a secure deployment Want to learn how to handle data pipelines at scale? Read our explanatory post on How Automated Data Labeling is Solving Large-Scale Challenges.   Pricing Deepchecks offers open-source and paid plans depending on the team’s security and support requirements. Arize Arize is an ML observability platform to help data scientists and ML engineers detect model issues, fix their underlying causes, and improve model performance. It allows teams to monitor, detect anomalies, and perform root cause analysis for model improvement. Arize dashboard It has a central inference store and comprehensive datasets indexing capabilities across environments (training, validation, and production), providing insights and making it easier to troubleshoot and optimize model performance. Benefits & Key Features Detect model issues in production Uses Vector Similarity Search to find problematic clusters containing outliers to fine-tune the model with high-quality data Automatic generation and sorting of clusters with semantically similar data points Best for Teams Who: Require real-time model monitoring for immediate feedback on model prediction and forecasting outcomes Pricing Arize offers a free tier for individuals and paid plans for small and global teams. What Should You Look For in an Outlier Detection Tool? Outlier detection is a crucial step in machine learning for ensuring data quality, accurate statistics, and reliable model performance. Various tools utilize different outlier detection algorithms and methods, so selecting the best tool for your dataset is essential. Consider the following factors when selecting an outlier detection tool: Ease of Use: Choose a user-friendly outlier identification solution that allows data scientists to focus on insights and analysis rather than a complex setup. Scalability: Select a solution that can efficiently handle enormous datasets, enabling real-time detection. Flexibility: Choose a platform that provides customizable options tailored to your unique data and outlier analysis use cases. This is essential for optimal performance. Visualizations: Select a platform that delivers clear and interactive visualizations to help you easily understand and analyze outlier data. Integration: Choose a tool that connects effortlessly to your existing data operations system, making it simple to incorporate outlier identification into your data processing and evaluation pipeline.

Aug 01 2023

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Med-PaLM: Google Research’s Medical LLM | Explained

Google has been actively researching the potential applications of artificial intelligence in healthcare, aiming to detect diseases early and expand access to care. As part of this research, Google built Med-PALM, the first AI system to obtain a pass mark on US Medical License Exam (USMLE) questions. At their annual health event, The Check Up, Google introduced their latest model, Med-PaLM 2, a large language model (LLM) designed for the medical domain that provides answers to medical questions.  Med-PaLM Med-PaLM 2 is able to score 85% on medical exam questions, an 18% improvement from the original Med-PaLM’s performance.  What is Med-PaLM? Med-PaLM is a large-scale generalist biomedical AI system that operates as a multimodal generative model, designed to handle various types of biomedical data, including clinical language, medical imaging, and genomics, all with the same set of model weights. The primary objective of Med-PaLM is to tackle a wide range of biomedical tasks by effectively encoding, integrating, and interpreting multimodal data. Med-PaLM leverages recent advances in language and multimodal foundation models, allowing for rapid adaptation to different downstream tasks and settings using in-context learning or few-shot fine-tuning. The development of Med-PaLM stems from the understanding that medicine is inherently multimodal, spanning text, imaging, genomics, and more. Unlike traditional AI models in biomedicine, which are often unimodal and specialized to execute specific tasks, Med-PaLM harnesses the capabilities of pretrained models and builds upon recent advancements in language and multimodal AI. The foundation of Med-PaLM is derived from three pretrained models: Pathways Language Model (PaLM) is a densely-connected, decoder-only, Transformer-based large language model, trained using the Pathways system. PaLM was trained on an extensive corpus of 780 billion tokens, encompassing webpages, Wikipedia articles, source code, social media conversations, news articles, and books. Vision Transformer (ViT) is an extension of the Transformer architecture designed to process visual data. Two ViT models with different parameter sizes are incorporated into Med-PaLM, each pretrained on a vast classification dataset consisting of approximately 4 billion images. PaLM-E is a multimodal language model that can process sequences of multimodal inputs, combining text, vision, and sensor signals. This model was built on pretrained PaLM and ViT models, and was initially intended for embodied robotics applications. PaLM-E demonstrated strong performance on various vision-language benchmarks. The integration of these pretrained models is accomplished through fine-tuning and aligning PaLM-E to the biomedical domain using the MultiMedBench dataset. MultiMedBench plays a pivotal role in the development and evaluation of Med-PaLM.  Med-PaLM is trained with a mixture of different tasks simultaneously, leveraging instruction tuning to prompt the model for various tasks using task-specific instructions, context information, and questions. For certain tasks, a "one-shot exemplar" is introduced to enhance instruction-following capabilities. During training, image tokens are interweaved with text tokens to create multimodal context input for the model. The resulting Med-PaLM model (with 12 billion, 84 billion, and 562 billion parameter variants) achieves remarkable performance on a wide range of tasks within the MultiMedBench benchmark, often surpassing state-of-the-art specialist models by a significant margin. Notably, Med-PaLM exhibits emergent capabilities such as zero-shot generalization to novel medical concepts and tasks, and demonstrates promising potential for downstream data-scarce biomedical applications. In addition to its performance, Med-PaLM has garnered attention for its ability to process inputs with multiple images during inference, allowing it to handle complex and real-world medical scenarios effectively. In addition to releasing a multimodal generative model, Google and DeepMind took the initiative to curate datasets and benchmarks for the development of biomedical AI systems. Moreover, they made these valuable resources openly accessible to the research community. MultiMedBench MultiMedBench is a multimodal biomedical benchmark that serves as a comprehensive and diverse evaluation dataset for medical AI applications. Developed as an essential component for training and evaluating generalist biomedical AI systems, MultiMedBench encompasses a wide array of tasks, spanning various data modalities, including clinical language, medical imaging, and genomics. The benchmark comprises 14 challenging tasks, including medical question answering, image interpretation, radiology report generation, and genomic variant calling. These tasks represent real-world medical complexities and demand multimodal data processing and reasoning capabilities. Towards Generalist Biomedical AI MultiMedBench standardizes the evaluation, comparison, and advancement of AI models in the biomedical domain. It fosters collaboration and transparency through open-source access, encouraging reproducibility and knowledge sharing in AI research for medicine. This benchmark represents a significant step forward in developing versatile AI systems with potential applications ranging from scientific discovery to improved healthcare delivery. MultiMedQA MultiMedQA is a comprehensive collection of multiple-choice medical question-answering datasets, used for training and evaluating Med-PaLM. MultiMedQA is comprised of the following datasets: MedQA, MedMCQA, and PubMedQA. These question-answering tasks are language-only and do not involve the interpretation of additional modalities such as medical imaging or genomics. The training set consists of 10,178 questions from MedQA and 182,822 quotations from MedMCQA. The test set contains 1,273 questions from MedQA, 4,183 questions from MedMCQA, and 500 questions from PubMedQA. Note: Med-PaLM was trained on MedQA and MedMCQA while PubMedQA was solely used for evaluation purposes. HealthSearchQA HealthSearchQA is a curated free-response dataset comprised of 3,375 commonly searched consumer medical questions. The dataset was carefully assembled using medical conditions and their associated symptoms — publicly-available commonly searched questions related to medical conditions were retrieved from search engine results pages and compiled to form the HealthSearchQA dataset. This dataset is designed as an open benchmark for consumer medical question answering, aiming to reflect real-world concerns and inquiries that consumers often have about their health. The questions in HealthSearchQA cover a wide range of topics, including queries about medical conditions, symptoms, and possible implications. Towards Expert-Level Medical Question Answering with Large Language Models Each question in HealthSearchQA is presented in a free-text format, devoid of predefined answer options, making it an open-domain setting. The dataset is curated to assess the clinical knowledge and question-answering capabilities of large language models in the context of consumer-oriented medical questions. While HealthSearchQA add valuable consumer medical question data to the benchmark, it is not exhaustive.    Med-PaLM: Results SOTA vs. Med-PaLM Med-PaLM consistently performs near or exceeds state-of-the-art (SOTA) models on all tasks within the MultiMedBench benchmark, showcasing its effectiveness in handling diverse biomedical data modalities.  In order to assess Med-PaLM’s performance, two baseline models were considered: (i) prior SOTA specialist models for each of the MultiMedBench tasks and (ii) a baseline generalist model without any biomedical domain finetuning. The findings show that across the three model sizes, Med-PaLM achieved its best performance on five out of twelve tasks, surpassing previous state-of-the-art (SOTA) results. For the remaining tasks, Med-PaLM remained highly competitive with the prior SOTA models. Towards Generalist Biomedical AI These results were achieved using a generalist model with the same set of model weights, without any task-specific architecture customization or optimization.  Ablations Google researchers conducted ablation studies to investigate the impact of scale and task joint training on the performance and capabilities of Med-PaLM. The aim was to understand the significance of different factors in achieving superior results and potential for positive task transfer. The first ablation study focused on assessing the importance of scale in generalist multimodal biomedical models. The findings revealed that larger models, with higher-level language capabilities, are particularly beneficial for tasks that require complex reasoning, such as medical (visual) question answering. This highlights the advantages of larger-scale models in handling diverse biomedical tasks effectively. The second ablation study investigated the evidence of positive task transfer resulting from joint training a single generalist model to solve various biomedical tasks. To evaluate this, a Med-PaLM variant was trained without including the MIMIC-CXR classification tasks in the task mixture. This variant was then compared to the Med-PaLM variant trained on the complete MultiMedBench mixture in the chest X-ray report generation task. The results demonstrated that joint training across modalities and tasks leads to positive task transfer. Human Evaluation Results In addition to the automated metrics, the human evaluation of Med-PaLM’s radiology report generation results shows promising outcomes. For this assessment, radiologists blindly ranked 246 retrospective chest X-rays, comparing the reports generated by Med-PaLM across different model scales to those produced by their fellow clinicians. Towards Generalist Biomedical AI The results indicate that in up to 40% of cases, the radiologists expressed a preference for the Med-PaLM reports over the ones generated by their human counterparts. Moreover, the best-performing Med-PaLM model exhibited an average of only 0.25 clinically significant errors per report.  Towards Generalist Biomedical AI These findings suggest that Med-PaLM’s reports are of high quality for clinical applications, showcasing its potential as a valuable tool for radiology report generation tasks across various model scales. Possible Harm To evaluate possible harm in medical question answering, Google researchers conducted a pairwise ranking analysis. Raters were presented with pairs of answers from different sources, such as physician-generated responses versus those from Med-PaLM-2, for a given question. The rates were then asked to assess the potential harm associated with each answer along two axes: the extent of possible harm and the likelihood of causing harm. Towards Expert-Level Medical Question Answering with Large Language Models The results above show that Med-PaLM 2's long-form answers demonstrate a remarkable level of safety, with a significant proportion of responses rated as having "No harm." This indicates that Med-PaLM 2 provides answers that were considered safe and low-risk according to the evaluation criteria.  This evaluation played a crucial role in assessing the safety and reliability of the answers provided by Med-PaLM 2 in comparison to those from human physicians. By considering the potential harm associated with different responses, the researchers gained valuable insights into the safety and risk factors of using AI-generated medical information for decision-making in real-world scenarios. Bias for Medical Demographics The evaluation of bias for medical demographics involved a pairwise ranking analysis to assess whether the answers provided by different sources exhibited potential bias towards specific demographic groups. Raters were presented with pairs of answers and asked to determine if any information in the responses was biased towards certain demographics. For instance, raters assessed if an answer was applicable only to patients of a particular sex. They looked for any indications of favoring or excluding specific demographic groups, which could result in unequal or inadequate medical advice based on factors such as age, gender, ethnicity, or other demographic characteristics. This evaluation was crucial in understanding if AI-generated medical information, like that from Med-PaLM 2, exhibited any demographic bias that could impact the quality and relevance of the answers provided to different patient populations. Identifying and addressing potential bias is essential to ensure fair and equitable healthcare delivery and to improve the overall reliability of AI-based medical question-answering systems. Med-PaLM: Limitations Med-PaLM has demonstrated its capabilities as a generalist biomedical AI system that can handle diverse medical modalities. It achieves close to or surpasses prior state-of-the-art (SOTA) results on various tasks, and generalizes to unseen biomedical concepts. However, there are some limitations and considerations to be acknowledged: MultiMedQA While MultiMedBench is a step towards addressing the need for unified benchmarks, it has certain limitations, including (i) the relatively small size of individual datasets (cumulative size of ~1 million samples) and (ii) limited modality and task diversity. The benchmark lacks certain life sciences data like transcriptomics and proteomics. LLM Capabilities Med-PaLM exhibits limitations in its language and multimodal capabilities. While it performs well on medical question answering tasks, there are challenges in measuring alignment with human answers. The current rating rubric may not fully capture dimensions like empathy conveyed in responses. The comparison between model outputs and physician answers lacks specific clinical scenarios, leading to potential limitations in generalizability. The evaluation is also constrained by the single-answer approach by physicians and longer model-generated responses. The evaluation with adversarial data for safety, bias, and equity considerations is limited in scope and requires expansion to encompass a wider range of health equity topics and sensitive characteristics. Ongoing research is necessary to address these limitations and ensure Med-PaLM's language and multimodal capabilities are robust and clinically applicable. Fairness & Equity Considerations Med-PaLM's limitations on fairness and equity considerations arise from the need for continued development in measuring alignment of model outputs. The current evaluation with adversarial data is limited in scope and should not be considered a comprehensive assessment of safety, bias, and equity. To address fairness concerns, future work should systematically expand adversarial data to cover a broader range of health equity topics and facilitate disaggregated evaluation over sensitive characteristics. Moreover, Med-PaLM's performance on medical question answering tasks might not fully account for potential biases in the data or model predictions. Careful considerations are necessary to ensure that the model's outputs do not perpetuate bias or discrimination against certain demographic groups. This involves investigating the impact of model decisions on different populations and identifying potential sources of bias. It is essential to approach fairness and equity considerations with a deeper understanding of the lived experiences, expectations, and assumptions of both the model's users and those generating and evaluating physician answers. Understanding the backgrounds and expertise of physicians providing answers and evaluating those answers can contribute to a more principled comparison of model outputs with human responses. Ethical Considerations Med-PaLM's ethical considerations include ensuring patient privacy and data protection, addressing potential biases and ensuring fairness in its outputs, ensuring safety and reliability for medical decision-making, providing interpretability for trust-building, conducting rigorous clinical validation, obtaining informed consent from patients, establishing transparent guidelines and accountability for its use in healthcare. Collaboration among AI researchers, medical professionals, ethicists, and policymakers is essential to address these concerns and ensure responsible and ethical deployment of Med-PaLM in medical settings. AI in Healthcare AI has emerged as a promising force in healthcare, transforming medical practices and improving patient outcomes. The applications span medical image analysis, disease diagnosis, drug discovery, personalized treatment plans, and more. Here are some of the ways leading AI Medical teams have improved their workflows and pushed forward their growth: Stanford Medicine cut experiment duration time from 21 to 4 days while processing 3x the number of images in 1 platform rather than 3  King’s College London achieved a 6.4x average increase in labeling efficiency for GI videos, automating 97% of the labels and allowing their annotators to spend time on value-add tasks Memorial Sloan Kettering Cancer Center built 1000, 100% auditable custom label configurations for its pulmonary thrombosis projects Med-PaLM: Conclusion Google’s Med-PaLM and Med-PaLM 2 represent groundbreaking advancements in AI for healthcare. These powerful generative AI language models demonstrate impressive capabilities in medical question-answering and handling diverse biomedical tasks. The development of benchmarks like MultiMedBench and MultiMedQA fosters collaboration and transparency in biomedical AI research. However, challenges remain, including fairness, ethical considerations, and limitations in large language model (LLM) capabilities, which will only increase as these applications become more widespread.

Jul 31 2023

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Image Embeddings to Improve Model Performance

We rely on our senses to perceive and communicate. We view the world through our eyes, hear using our ears, and speak using our mouths. But how do algorithms achieve such incredible feats without these sensory experiences? The secret lies in embeddings! Embeddings enable computers to understand and analyze data through numerical representations. An embedding model transforms the complexity of visual data into a condensed, numerical representation - the embedding vector. These embedding vectors hold the essence of images, capturing their unique features, patterns, and semantics. Machine learning models gain insight into images by leveraging image embeddings. This paves the way for enhanced image classification, similarity comparison, and image search capabilities. 💡 Want to learn more about embeddings in machine learning? Read The Full Guide to Embeddings in Machine Learning. What are Image Embeddings? To extract information from images, researchers use image embeddings to capture the essence of an image. Image embeddings are a numerical representation of images encoded into a lower-dimensional vector representation. Image embeddings condense the complexity of visual data into a compact form. This makes it easier for machine learning models to process the semantic and visual features of visual data. These embedding representations are typically in the form of fixed-length vectors, which are generated using deep learning models, such as Convolutional Neural Networks (CNNs) like ResNet.  Images are created by combining pixels, with each pixel containing unique information. For machine learning models to understand the image, each pixel needs to be represented as an image embedding. How to Create Image Embeddings There are various processes of generating image embeddings, used to capture the essence of an image. These processes enable tasks like image classification, similarity comparison, and image search. Convolutional Neural Networks A Comprehensive Guide to Convolutional Neural Networks Convolutional Neural Networks are a fundamental network architecture in deep learning. The core elements of CNNs include convolutional layers, pooling layers, and fully connected layers. CNNs can serve as both standalone models and as components that enhance the capabilities of other models. As a standalone model, CNNs are specifically tailored to process and analyze grid-like data directly. They can also be used as feature extractors or pre-trained models to aid other models. CNNs excel at pattern recognition and object identification within visual data by applying convolutional filters to extract low-level features. These low-level features within an image will be combined to identify high-level features within the visual data. 💡 To learn more about CNNs, read our Convolutional Neural Networks Overview. Unsupervised Machine Learning Unsupervised learning is a machine learning technique in which algorithms learn patterns from unlabeled data. This can be applied to image embeddings to further optimise representation by identifying clusters or latent factors within the embeddings without annotations.  Clustering is a popular unsupervised learning method in which you group similar images together using algorithms. Dimensionality reduction is another technique that involves transforming data from a high-dimensional space into a low-dimensional space. To accomplish this, you could use techniques like Principal Component Analysis (PCA) to transform the embeddings into lower-dimensional spaces and identify unique underlying relationships between images. Pre-trained Networks and Transfer Learning Pre-trained networks are CNN models trained on large networks such as ImageNet. These pre-trained models already possess the knowledge base of different representations of images and can be used to create new image embeddings.  Machine learning engineers do not need to build a model from scratch. They can use pre-trained models to solve their task or can fine-tune them for a specific job. An example of where to use pre-trained networks is in tasks such as image classification. Detecting People in Artwork with CNNs Transfer learning is a machine learning method where the application of knowledge obtained from a model used in one task can be reused as a foundation point for another task. It can lead to better generalisation, faster convergence, and improved performance on the new task.  You can use both methods to improve image embedding for specific and new tasks.  Benefits of Image Embeddings Image embeddings offer several benefits in the world of computer vision.  Numerical Representation Image embeddings offer a compact numerical representation of images that helps machine learning models better understand and learn from the data. Compacting the data into numerical representation saves storage space, reduces memory requirements, and facilitates efficient processing and analysis of the image. Semantic Information Image embeddings provide semantic information by capturing low-level visual features, such as edges, and textures, and higher-level semantic information, such as objects. They encode an image's meaningful features, allowing models to interpret the image's content easily. Semantic information is crucial for image classification and object detection tasks.  💡 To learn more about Object Detection, read Object Detection: Models, Use Cases, Examples. Transfer Learning When using a pre-trained model to generate image embeddings, the weights from the pre-trained models will be transferred, and the embeddings can use that as a starting point with new tasks. Learned image embedding representations have shown higher model performance, even if it comes across unseen or unknown data.  Improved Performance As image embeddings reduce the dimensionality of the image data, the lower-dimensional vector representations also reduce the visual features' complexity. Therefore, the model will require less memory requirements, improving its performance in processing data and faster training, all while still being able to contain the essential information necessary for the task at hand. Tools Required for Image Embedding This section will discuss the essential tools required for creating image embeddings, enabling you to extract powerful representations from images for various computer vision tasks. Deep Learning Frameworks Deep learning frameworks offer building blocks for designing, training, and validating deep neural networks. They provide powerful tools and libraries for different tasks, including computer vision, natural language processing, language models, and more. The most popular deep learning frameworks are: TensorFlow - provides comprehensive support for building and training deep learning models and computing image embeddings. It offers high-level APIs like Keras so that you can easily build and train your model. This  provides flexibility and control over tasks. PyTorch is another popular deep learning framework that is well recognized for its easy-to-understand syntax. It provides a seamless integration with Python, containing various tools and libraries to build, train, and deploy machine learning models.  Keras is a high-level deep learning framework that runs on top of backend engines such as TensorFlow. Keras Core, which will be available in Fall 2023, will also support PyTorch and Jax. Keras provides a variety of pre-trained models to improve your model's performance with feature extraction, transfer learning, and fine-tuning. Image Embeddings to Improve Model Performance There are several methods, techniques and metrics in computer vision and image embeddings to improve your model's performance.  Similarity Similarity is used on image embeddings to plot points onto a dimensional space, in which these points explore similar images based on how close they are in the pixel region. You can use various similarity metrics to measure the distance between points. These metrics include: Euclidean distance - the length of a line segment between two points. A smaller Euclidean distance indicates more significant similarity between the image embeddings.  Cosine similarity - focuses on the angle between vectors rather than the distance between their ends. The angle between the vectors and a value between -1 and 1 will be calculated to indicate the similarities between the embeddings. One (1) represents that the embeddings are identical, and -1 represents otherwise.  K Nearest Neighbours - used for both regression, classification tasks, and to make predictions on the test set based on the training dataset's characteristics (labeled data). Depending on the chosen distance metric, the distance between the test set and the training dataset assumes that similar characteristics or attributes of the data points exist within proximity.  Other similarity metrics include Hamming distance and Dot product.  Principal Component Analysis As mentioned above, you can use Principal Component Analysis (PCA) to create image embeddings and improve model performance. PCA is used to reduce the dimensionality of large datasets. This is done by transforming a large set of variables into smaller ones while preserving the most essential variations and patterns. Principal Component Analysis (PCA) Explained Visually with Zero Math There are several ways that PCA is used to improve model performance: Feature Vectors are the numerical representations that capture the visual and semantic characteristics of images. Feature vectors are concatenated representations that contain meaningful information about images in a lower-dimensional space using PCA and other features such as textual descriptions or metadata. Noise Reduction is a major challenge in images as they contain many pixels, making them more susceptible to noise variations. PCA can filter out noise and reduce irrelevant information while retaining the vital information. This increases the model's robustness against noise and improves its ability to generalize to unseen data.  Interpretability reduces the number of variables and enables a linear transformation from the original embeddings in the large dataset to a new, reduced dataset. This allows improved interpretability of visual and semantic characteristics, identify relationships, and uncover significant features and patterns within the image data.  Hyperparameter Tuning Hyperparameters are the parameters that define the architecture of a model . You tweak the hyperparameters to create the ideal model architecture for optimal performance. You ideally select hyperparameters that have a significant impact on the model's performance. For example: Learning Rate is a hyperparameter that controls the value to change the model’s weights in response to the estimated error each time they are updated. This influences the speed and convergence of the training process. You should aim to find the optimal balance for training the model with image embeddings. Batch Size refers to the number of samples used in each training iteration. For example, a large batch size can have faster training but require more GPU memory. A smaller batch size can have better generalization but slower convergence. Batch size greatly affects the model's training speed, generalization, memory consumption, and overall performance.  Optimization algorithms are used to find the best solution to a problem. For example, algorithms such as Adam and Stochastic Gradient Descent (SGD) have different properties, meaning they have different behaviors. You should experiment with varying algorithms of optimization and hyperparameter tuning to find the optimal performance when training image embeddings. Choosing the correct optimization algorithm and fine-tuning it will impact the speed and quality of the model's convergence.  Other techniques for hyperparameter tuning in image embeddings include regularisation techniques, activation functions, early stopping, and grid/random search. Transfer Learning Incorporating a Novel Dual Transfer Learning Approach for Medical Images Taking the knowledge from one task and applying it to another can help your model generalize better, produce faster convergence, and enhance overall performance. Below are different methods of how transfer learning can do this: Feature Extraction - uses pre-trained models that have been trained on large datasets and allows you to utilize the weights on visual representation. Pre-trained models act as a fixed feature extractor in which your model can capture visual insights of other image datasets or unseen data to improve model performance.  Reduction in Training Time - transfer learning is an excellent way to improve your machine learning workflow, as building a model from scratch can be time-consuming and computationally expensive. Transfer learning of optimally trained models built using large datasets saves you from investing in more GPU, time, and employees. Training a new model with a pre-trained model with weights means that the new model requires fewer iterations and less data to achieve an optimal performance.  Generalization - the ability of your model to adapt to new or unseen data. Pre-trained models have been developed using diverse datasets, allowing them to generalize better to a wide range of data. Using pre-trained models will enable you to adapt the robustness from the model to yours so it  performs well on unseen and new images.  Fine Tuning - transfer learning of pre-trained models allows you to fine-tune the model to a specific task. Updating the weights of a pre-trained model using a smaller dataset specific to a particular task will allow the model to adapt and learn new features regarding the task quickly.  Large Datasets Using large image datasets can vastly improve your model's performance. Large image datasets provide a diverse range of images that allow the model to analyze and identify patterns, object variations, colors, and textures. Overfitting is a problem that many data scientists and machine learning engineers face when working with machine learning models. Large datasets overcome the challenge of overfitting as they have more diverse data to generalise better and capture features and patterns.  The model becomes more stable with a larger dataset, encountering fewer outliers and noise. It can leverage patterns and features from a diverse range of images rather than solely focusing on individual instances in a smaller dataset. When working with larger datasets, you can effectively reap the benefits of model performance when you implement transfer learning. Larger datasets allow pre-trained models to identify better and capture visual features, which they can transfer to other image-related tasks.  Image Embeddings: Key Takeaways Image embeddings compress images into lower-dimensional vector representations, providing a numerical representation of the image content. You can generate image embeddings through different methods, such as CNNs, unsupervised learning, pre-trained networks, and transfer learning. Image embeddings used in computer vision provide machine learning models with numerical representation, semantic information, improved performance, and transfer learning capabilities.  Various techniques can improve model performance using image embeddings, such as similarity, PCA, hyperparameter tuning, transfer learning, and the use of larger datasets. 

Jul 27 2023

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KL Divergence in Machine Learning

Kullback-Leibler (KL) divergence, or relative entropy, is a metric used to compare two data distributions. It is a concept of information theory that contrasts the information contained in two probability distributions. It has various practical use cases in data science, including assessing dataset and model drift, information retrieval for generative models, and reinforcement learning. This article is an intro to KL divergence, its mathematics, how it benefits machine learning practitioners, and when it should and should not be used. Divergence in Statistics The divergence between two probability distributions quantifies how much the two differ from each other. Probability Distribution A probability distribution models the values that a random variable can take. It is modeled using parameters including mean and variance. Changing these parameters gives us different distributions and helps us understand the random numbers' spread in a given latent space. There are various algorithms for divergence measures, including: Jensen-Shannon Divergence Hellinger Distance Total Variation Divergence Kullback-Leibler Divergence This article is focused on the KL divergence, so let’s visit the mathematics behind the scenes. Mathematics behind Kullback-Leibler KL Divergence KL divergence is an asymmetric divergence metric. Asymmetric means that given a probability distribution P and a probability distribution Q, the divergence between P and Q will not be the same as Q and P. KL divergence is defined as the number of bits required to convert one distribution into another. The lower bound value is zero and is achieved when the distributions under observation are identical. It is often denoted with the following notation: D KL(P||Q), and the formula for the metric is In the formula above: X is a possible event in the probability space. P(x) and Q(x) are the probabilities of x in the distribution of P and Q, respectively. The ratio P(x)/Q(x) represents the relative likelihood of event "x" according to P compared to Q. The formula shows that KL divergence closely resembles the cross-entropy loss function used in deep learning. This is because both KL divergence and cross-entropy are quantification of the differences between different data distributions and are often interchangeable. Let's expand the equation to understand the relationship further. The term to the right is the entropy H(X) of the probability distribution P(X) while that to the left is the cross-entropy between P(X) and P(Q). Applications of KL Divergence in Data Science Optimizing KL Divergence accuracy in machine learning hinges on selecting the best Python hosting service, necessitating ample computational power, customized ML environments, and seamless data integration to streamline model training and inference processes effectively. Monitoring Data Drift One of the most common use cases of KL divergence in machine learning is to detect drift in datasets. Data is constantly changing, and a metric is required to assess the significance of the changes. Constantly monitoring data allows machine learning teams to decide whether automated tasks such as model re-training are required. It also provides insights regarding the variable nature of the data under consideration and helps draw statistical analysis. KL divergence is applied to data in discrete form by forming data bins. The data points are binned according to the features to form discrete distributions, i.e., each feature is independently processed for divergence calculation. The divergence scores for each bin are summed up to get a final picture. Loss Function for Neural Networks While regression problems use the mean-squared error (MSE) loss function for the maximum likelihood estimation, a classification problem works with probabilistic distributions. A classification neural network is coupled with the KL divergence loss function to compare the model outputs to the true labels.  For example, a binary cat classifier predicts the probabilities for an image as p(cat) = 0.8 and p(not cat) = 0.2. If the ground truth of the image is that it is a cat, then the true distribution becomes p(cat) = 1 and p(not cat) = 0. We now have two different distributions modeling the same variable. The neural network aims to bring these predicted distributions as close to the ground truth as possible. Taking values from our example above as P(X) = {1,0} and Q(X) = {0.8, 0.2}, the divergence would be: Most of the terms above will be zeroed out and the final result comes out to be: When using KL divergence as the loss, the network optimizes to bring the divergence value down to zero. Variational Auto-Encoder Optimization An auto-encoder is a neural network architecture that encodes an input image onto an embedding layer. Variational auto-encoders (VAEs) are a specialized form of traditional architecture that project the input data onto a probability distribution (usually a Gaussian distribution). Variational Autoencoders The VAE architecture involves two loss functions, a mean-squared error (MSE) to calculate the loss between the output image and the ground truth and the KL divergence to calculate the statistical distance between the true distribution and the approximating distribution in the middle. Interested in encodings? Here’s our Full Guide to Embeddings. Generative Adversarial Networks The Generative Adversarial Networks (GANs) consist of two networks training to outdo each other (adversaries). One of the networks trains to output synthetic (fake) data indistinguishable from the actual input, while the other learns to detect synthetic images from actual. Google: Overview of GAN Structure The datasets involved in GANs (fake and real) are two distributions the network tries to compare. The comparison uses KL divergence to create a comparable metric to evaluate whether the model is learning. The discriminator tries to maximize the divergence metric, while the generator tries to minimize it, forming an overall min-max loss configuration. Moreover, it is essential to mention that KL divergence does have certain drawbacks, such as its asymmetric behavior and unstable training dynamics, that make the Jensen-Shannon Divergence a better fit. Limitations of KL Divergence As previously established, KL divergence is an asymmetric metric. This means that it can not be used as strictly a distance measure since the distance between two entities remains the same from either perspective. Moreover, if the data samples are pulled from distributions that use different parameters (mean and variance), KL divergence will not yield reliable results. In this case, one of the distributions needs to be adjusted to match the other. KL Divergence: Key Takeaways Divergence is a measure that provides the statistical distance between two distributions. KL divergence is an asymmetric divergence metric defined as the number of bits required to convert one distribution into another. A zero KL divergence score means that the two distributions are exactly the same. A higher score defines how different the two distributions are. KL divergence is used in artificial intelligence as a loss function to compare the predicted data with true values. Some other AI applications include generative adversarial networks (GANs) and data model drifting.

Jul 26 2023

sampleImage_synthetic-data-generation

What Is Synthetic Data Generation and Why Is It Useful

The conversation around synthetic data in the machine learning field has increased in recent years. This is attributable to (i) rising commercial demands that see companies trying to obtain and leverage greater volumes of data to train machine learning models. And (ii) the fact that the quality of generated synthetic data has advanced to the point where it is now reliable and actively useful.  Companies use synthetic data in different stages of their artificial intelligence development pipeline. Processes like data analysis, model building, and application creation can be made more time and cost-efficient with the adoption of synthetic data. In this article, we will dive into:  What is Synthetic Data Generation? Why is Synthetic Data Useful? Synthetic Data Applications and Use Cases Synthetic Data: Key Takeaways What is Synthetic Data Generation? What comes to mind when you think about “synthetic data”? Do you automatically associate it with fake data? Further, can companies confidently rely on synthetic data to build real-life data science applications? Synthetic data is not real-world data, but rather artificially created fake data. It is  generated through the process of “synthesis,” using models or simulations instead of being collected directly from real-life environments. Notably, these models or simulations can be created using real, original data.  To ensure satisfactory usability, the generated synthetic data should have comparable statistical properties to the real data. The closer the properties of synthetic data are to the real data, the higher the utility. Synthesis can be applied to both structured and unstructured data with different algorithms suitable for different data types. For instance, variational autoencoders (VAEs) are primarily employed for tabular data, and neural networks like generative adversarial networks (GANs) are predominantly utilized for image data. Data synthesis can be broadly categorized into three main categories: synthetic data generated from real datasets, generated without real datasets, or generated with a combination of both.   Generated From Real Datasets Generating synthetic data using real datasets is a widely employed technique. However, it is important to note that data synthesis does not involve merely anonymizing a real dataset and converting it to synthetic data. Rather, the process entails utilizing a real dataset as input to train a generative model capable of producing new data as output. Data synthesized using real data The data quality of the output will depend on the choice of algorithm and the performance of the model. If the model is trained properly, the generated synthetic data should exhibit the same statistical properties as the real data. Additionally, it should preserve the same relationships and interactions between variables as the real dataset.  Using synthetic datasets can increase productivity in data science development, alleviating the need for access to real data. However, a careful modeling process is required to ensure that generated synthetic data is able to represent the original data well. A poorly performing model might yield misleading synthetic data. Applications and distribution of synthetic data must prioritize privacy and data protection. Generated Without Real Data Synthetic data generation can be implemented even in the absence of real datasets. When real data is unavailable, synthetic data can be created through simulations or designed based on the data scientist’s context knowledge. Simulations can serve as generative models that create virtual scenes or environments, from which synthetic data can be sampled. Additionally, data collected from surveys can also be used as indirect information to craft an algorithm that generates synthetic data.  If a data scientist has domain expertise in certain use cases, this knowledge can be applied to generate new data based on valid assumptions. While this method does not rely on real data, an in-depth understanding of the use case is crucial to ensure that the generated synthetic data has characteristics consistent with the real data. In situations where high-utility data is not required, synthetic data can be generated without real data or domain expertise. In such cases, data scientists can create “dummy” synthetic data; however, it may not have the same statistical properties as the real data. Data synthesized without real data Synthetic Data and Its Levels of Utility The utility of generated synthetic data varies across different types. High-utility synthetic data closely aligns with the statistical properties of real data, while low-utility synthetic data may not necessarily represent the real data well. The specific usage of synthetic data determines the approach and effort that goes into generating it. Different types of synthetic data generation and their levels of utility Why is Synthetic Data Useful? Adopting the use of synthetic data has the potential to enhance a company's expansion and profits. Let’s look at the two main benefits of using synthetic data: making data access easier and speeding up data science progress. Making Data Access Easier Data is an essential component of machine learning tasks. However, the data collection process can be time-consuming and complicated, particularly when it comes to collecting personal data. To satisfy data privacy regulations, additional consent to use personal data for secondary purposes is required and might pose feasibility challenges and introduce bias to the data collected.  To simplify the process, data scientists might opt to use public or open-source datasets. While these resources can be useful, public datasets might not align with specific use cases. Additionally, relying solely on open-source datasets might affect model training, as they do not encompass a sufficient range of data characteristics.  💡 Learn more about Top 10 Open Source Datasets for Machine Learning. Using synthetic data can be a useful alternative to solve data access issues. Synthetic data is not real data, and it does not directly expose the input data used to train the generative model. Given this, using synthetic data has a distinct advantage in that it is less likely to violate privacy regulations and does not need consent in order to be used. Moreover, generative models can generate synthetic data on demand that covers any required spectrum of data characteristics. Synthetic data can be used and shared easily, and high-utility synthetic data can be relied upon as a proxy for real data. In this context, services like Proxy-Store exemplify the importance of maintaining high standards of data privacy and security, ensuring that synthetic data serves as a safe and effective substitute for real datasets Speeding Up Data Science Progress Synthetic data serves as a viable alternative in situations when real data does not exist or certain data ranges are required. Obtaining rare cases in real data might require significant time and may yield insufficient samples for model training. Furthermore, certain data might be impractical or unethical to collect in the real world.  Synthetic data can be generated for rare cases, making model training more heterogenous and robust, and expediting access to certain data. Synthetic data can also be used in an exploratory manner before a data science team invests more time in exploring big datasets. When crucial assumptions are validated, the team can then proceed with the essential but time-consuming process of collecting real data and developing solutions. Moreover, synthetic data can be used in initial model training and optimization before real data is made available. Using transfer learning, a base model can be trained using synthetic data and optimized with real data later on. This saves time in model development and potentially results in better model performance.  In cases where the use of real data is cumbersome, high-utility synthetic data can represent real data and obtain similar experiment results. In this case, the synthetic data acts as a representation of the real data.  Adopting synthetic data for any of these scenarios can save time and money for data science teams. With synthetic data generation algorithms improving over time, it will become increasingly common to see experiments done solely using synthetic data. How Can We Trust the Usage of Synthetic Data? In order to adopt and use generated synthetic data, establishing trust in its quality and reliability is essential.  The most straightforward approach to achieve this trust in synthetic data is to objectively assess if using it can result in similar outcomes as using real data. For example, data scientists can conduct parallel analysis using synthetic data and real data to determine how similar the outcomes are. For model training, a two-pronged approach can be employed. Data scientists can first train a model with the original dataset, and then train another model by augmenting the original dataset with synthetic inputs of rare cases. By comparing the performance of both models, data scientists can assess if the inclusion of synthetic data improves the heterogeneity of the dataset and results in a more robust and higher-performing model. It is also important to compare the data privacy risk for using real and synthetic datasets throughout the machine learning pipeline. When it is assured that both data quality and data privacy issues are addressed, only then can data practitioners, business partners, and users trust the system as a whole. 💡 Interested in learning about AI regulations? Learn more about What the European AI Act Means for AI developers. Applications of Synthetic Data In this section, we are going to look at several applications of synthetic data: Retail  In the retail industry, automatic product recognition is used to replenish products on the shelf, at the self-checkout system, and as assistance for the visually impaired. To train machine learning models effectively for this task, data scientists can generate synthetic data to supplement their datasets with variations in lighting, positions, and distance to increase the model's ability to recognize products in real-world retail environments. 💡 Neurolabs uses synthetic data to train computer vision models. Leveraging Encord Active and the quality metrics feature, the team at Neurolabs was able to identify areas of improvement in their synthetic data generation process in order to improve model performance across various use cases. Improving synthetic data generation with Encord Active – Neurolabs Manufacturing and Distribution Machine learning algorithms coupled with sensor technology can be applied in industrial robots to perform a variety of complex tasks for factory automation. To reliably train AI models for robots, it is essential to collect comprehensive training data that covers all possible anticipated scenarios.  NVIDIA engineers developed and trained a deep learning model for a robot to play dominoes. Instead of creating training data manually, a time and cost-intensive process, they chose to generate synthetic data.  The team simulates a virtual environment using a graphics-rendering engine to create images of dominos with all possible settings of different positions, textures, and lighting conditions. This synthetic data is used to train a model, which enables a robot to successfully recognize, pick up, and manipulate dominoes. Synthesized images of dominos – NVIDIA Healthcare Data access in the healthcare industry is often challenging due to strict privacy regulations for personal data and the time-consuming process of collecting patient data.  Typically, sensitive data needs to be de-identified and masked with anonymization before it can be shared. However, the degree of data augmentation required to minimize re-identification risk might affect data utility.  Using synthetic data as a replacement for real data makes it possible to be shared publicly as it often fulfills the privacy requirement. The high-utility profile of the synthetic dataset makes it useful for research and analysis.  Financial Services In the financial services sector, companies often require standardized data benchmarks to evaluate new software and hardware solutions. However, data benchmarks need to be established to ensure that these benchmarks cannot be easily manipulated. Sharing real financial data poses privacy concerns. Additionally, the continuous nature of financial data necessitates continuous de-identification, adding to implementation costs. Using synthetic data as a benchmark allows for the creation of unique datasets for each solution provider to submit honest, comparable outputs. Synthetic datasets also preserve the continuous nature of the data without incurring additional costs as the synthetic datasets have the same statistical properties and structure as the real data. Companies can also test available products on the market using benchmark data to provide a consistent evaluation of the strengths and weaknesses of each solution without introducing bias from the vendors. For software testing, synthetic data can be a good solution, especially for applications such as fraud detection. Large volumes of data are needed to test for scalability and performance of the software but high-quality data is not necessary.  Transportation Synthetic data is used in the transportation industry for planning and policymaking. Microsimulation models and virtual environments are used to create synthetic data, which then trains machine learning models.  By using virtual environments, data scientists can create novel scenarios and analyze rare occurrences, or situations where real data is unavailable. For instance, a planned new infrastructure, such as a new bridge or new mall, can be simulated before being constructed. For autonomous vehicles, such as self-driving cars, high utility data is required, particularly for object identification.  The sensor data is used to train models that recognize objects along the vehicle’s path.. Using synthetic data for model training allows for the capture of every possible scenario, including rare or dangerous scenarios not well documented in actual data. It not only models real-world environments but also creates new ones to make the model respond to a wide range of different behaviors that could potentially occur. Sample images (original and synthetic) of autonomous vehicle view – KITTI Synthetic Data: Key Takeaways Synthetic data is data that is generated from real data and has the same statistical properties as real data. Synthetic data makes data access easier and speeds up data science progress.  The application of synthetic data can be applied in various industries, such as retail, manufacturing, healthcare, financial services, and transportation. Use cases are expected to grow over time. Neurolabs uses Encord Active and its quality metrics to improve the synthetic data generation process of their image recognition solution and improve their model performance.

Jul 25 2023

sampleImage_convolutional-neural-networks-explained

Convolutional Neural Networks (CNN) Overview

Convolutional Neural Networks (CNNs) are a powerful tool for image analysis that can be used for tasks such as image classification, object detection, and semantic segmentation.  As defined by Aparna Goel “A Convolutional Neural Network  is a type of deep learning algorithm that is particularly well-suited for image recognition and processing tasks. It is made up of multiple layers, including convolutional layers, pooling layers, and fully connected layers.”  In this article, we will dive into: Basics of Convolutional Neural Networks Convolution and Filter Application Pooling Operations & Techniques Activation Functions in CNNs Architectures and Variants of CNNs Tips and Tricks for Training CNNs Applications of CNNs CNN: Key Takeaways Basics of Convolutional Neural Networks CNNs work by extracting features from images using convolutional layers, pooling layers, and activation functions. These layers allow CNNs to learn complex relationships between features, identify objects or features regardless of their position, and reduce the computational complexity of the network.  Feature maps are a key concept in CNNs, which are generated by convolving filters over the input image, and each filter specializes in detecting specific features. The feature maps serve as the input for subsequent layers, enabling the network to learn higher-level features and make accurate predictions.  Parameter sharing is another critical aspect of CNNs. It allows the network to detect similar patterns regardless of their location in the image, promoting spatial invariance. This enhances the network's robustness and generalization ability. Understanding these key components of CNNs is essential for unlocking their full potential in visual data analysis. Convolutional Neural Networks vs Recurrent Neural Networks While Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are different architectures designed for different tasks, they share some similarities in their ability to capture and process sequential data. Both CNNs and RNNs operate on local receptive fields. CNNs and some variants of RNNs, such as Long Short-Term Memory (LSTM), utilize parameter sharing. CNNs are also being increasingly used in conjunction with other machine learning techniques, such as natural language processing. Convolutional Layer Convolutional layers in CNNs extract features from input images through convolution, filter application, and the use of multiple filters.  Convolution and Filter Application Convolution applies filters to the input image, sliding across and generating feature maps, while filters are small tensors with learnable weights, capturing specific patterns or features. Multiple filters detect various visual features simultaneously, enabling rich representation. Padding and Stride Choices Padding preserves spatial dimensions by adding pixels around the input image. Stride determines the shift of the filter's position during convolution. Proper choices control output size, spatial information, and receptive fields. Zero padding is a technique used in CNNs to maintain the spatial dimensions of feature maps when applying convolutional operations. It involves adding extra rows and columns of zeros around the input, which helps preserve the size and resolution of the feature maps during the convolution process and prevents information loss at the borders of the input data. Multiple Filters for Feature Extraction Each filter specializes in detecting specific patterns or features. Multiple filters capture diverse aspects of the input image simultaneously, while filter weights are learned through training, allowing adaptation to relevant patterns. The filter size in CNNs plays a crucial role in feature extraction, influencing the network's ability to detect and capture relevant patterns and structures in the input data. Understanding convolution, padding, stride, and multiple filters is crucial for feature extraction in CNNs, facilitating the identification of patterns, spatial information capture, and building robust visual representations. Pooling Operations Pooling layers are an integral part of Convolutional Neural Networks (CNNs) and play a crucial role in downsampling feature maps while retaining important features. In this section, explore the purpose of pooling layers, commonly used pooling techniques such as max pooling and average pooling, and how pooling helps reduce spatial dimensions. The Basic Structure of CNN Purpose of Pooling Layers  Pooling layers are different type of layers, primarily employed to downsample the feature maps generated by convolutional layers. The downsampling process reduces the spatial dimensions of the feature maps, resulting in a compressed representation of the input. By reducing the spatial dimensions, pooling layers enhance computational efficiency and address the problem of overfitting by reducing the number of parameters in subsequent layers. Additionally, pooling layers help in capturing and retaining the most salient features while discarding less relevant or noisy information. Pooling Techniques Two popular pooling techniques employed in CNNs are max pooling and average pooling.  Max pooling selects the maximum value from a specific window or region within the feature map. It preserves the most prominent features detected by the corresponding convolutional filters. Max Pooling Average pooling calculates the average value of the window, providing a smoothed representation of the features.  Both techniques contribute to reducing spatial dimensions while retaining important features, but they differ in their emphasis. Max pooling tends to focus on the most significant activations, while average pooling provides a more generalized representation of the features in the window. Reducing Spatial Dimensions and Retaining Important Features Pooling operations help reduce spatial dimensions while retaining crucial features in several ways. First, by downsampling the feature maps, pooling layers decrease the number of parameters, which in turn reduces computational complexity. This downsampling facilitates faster computation and enables the network to process larger input volumes efficiently. To add further context related to pooling operations in CNNs, it's worth mentioning that strides can serve the purpose of downsampling as well, particularly when the stride value is greater than 1. Second, pooling layers act as a form of regularization, aiding in preventing overfitting by enforcing spatial invariance. By aggregating features within a pooling window, pooling layers ensure that small spatial shifts in the input do not significantly affect the output, promoting robustness and generalization. Lastly, by selecting the maximum or average activations within each window, pooling layers retain the most important and informative features while reducing noise and irrelevant variations present in the feature maps. Understanding the purpose of pooling layers, the techniques employed, and their impact on downsampling and feature retention provides valuable insights into the role of pooling in CNNs. These operations contribute to efficient computation, regularization, and the extraction of important visual features, ultimately enhancing the network's ability to learn and make accurate predictions on complex visual data. Activation Functions in Convolutional Neural Networks (CNNs) Activation functions are essential in Convolutional Neural Networks (CNNs) as they introduce non-linearity, enabling the network to learn complex feature relationships. In this section, the popular activation functions used in CNNs, including ReLU, sigmoid, and tanh, are discussed. The properties, advantages, and limitations of these activation functions are explored, highlighting their significance in introducing non-linearity to the network. ReLU (Rectified Linear Unit)   ReLU is widely used, setting negative values to zero and keeping positive values unchanged. It promotes sparsity in activations, allowing the network to focus on relevant features. ReLU is computationally efficient and facilitates fast convergence during training. However, it can suffer from dead neurons and unbounded activations in deeper networks. Sigmoid  Sigmoid squashes activations between 0 and 1, making it suitable for binary classification tasks and capturing non-linearity within a limited range. It is differentiable, enabling efficient backpropagation, but is susceptible to the vanishing gradient problem and may not be ideal for deep networks. Tanh  Tanh maps activations to the range -1 to 1, capturing non-linearity within a bounded output. It has a steeper gradient than sigmoid, making it useful for learning complex representations and facilitating better gradient flow during backpropagation. However, it also faces the vanishing gradient problem. Activation Functions Activation functions are crucial as they introduce non-linearity, enabling CNNs to model complex relationships and capture intricate patterns. Non-linearity allows CNNs to approximate complex functions and tackle tasks like image recognition and object detection. Understanding the properties and trade-offs of activation functions empowers informed choices in designing CNN architectures, leveraging their strengths to unlock the network's full expressive power. By selecting appropriate activation functions, CNNs can learn rich representations and effectively handle challenging tasks, enhancing their overall performance and capabilities. Architectures and Variants of Convolutional Neural Networks (CNNs) Architecture of a traditional CNN (Convolutional Neural Network) has witnessed significant advancements over the years, with various architectures and variants emerging as influential contributions to the field of computer vision. In this section, notable CNN architectures, such as LeNet-5, AlexNet, VGGNet, and ResNet, are discussed, highlighting their unique features, layer configurations, and contributions. Additionally, popular variants like InceptionNet, DenseNet, and MobileNet are mentioned. LeNet-5  LeNet-5, introduced by Yann LeCun et al. in 1998, was one of the pioneering CNN architectures. It was designed for handwritten digit recognition and consisted of two convolutional layers, followed by average pooling, fully connected layers, and a softmax output layer. LeNet-5's lightweight architecture and efficient use of shared weights laid the foundation for modern CNNs, influencing subsequent developments in the field. Architecture of LeNet-5 Model AlexNet AlexNet developed by Alex Krizhevsky et al. in 2012, made significant strides in image classification. It won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012 and achieved a significant improvement over previous methods. AlexNet featured a deeper architecture with multiple convolutional layers, introduced the concept of ReLU activations, and employed techniques like dropout for regularization. Its success paved the way for the resurgence of deep learning in computer vision. Pre-Trained AlexNet Architecture VGGNet VGGNet, proposed by Karen Simonyan and Andrew Zisserman in 2014, introduced deeper architectures with up to 19 layers. VGGNet's key characteristic was the consistent use of 3x3 convolutional filters throughout the network, which enabled better modeling of complex visual patterns. The VGGNet architecture is known for its simplicity and effectiveness, and it has served as a baseline for subsequent CNN models. The VGG-16 Architecture ResNet ResNet developed by Kaiming He et al. in 2015, brought significant advancements in training very deep neural networks. ResNet introduced the concept of residual connections, allowing information to bypass certain layers and alleviating the vanishing gradient problem. This breakthrough enabled the successful training of CNNs with 50, 100, or even more layers. ResNet's skip connections revolutionized deep learning architectures and enabled the training of extremely deep and accurate networks. The ResNet50 Model Some other notable variants are InceptionNet, DenseNet, and MobileNet that have made significant contributions to the field of computer vision. InceptionNet introduced the concept of inception modules,DenseNet featured dense connections between layers and MobileNet focused on efficient architectures for mobile and embedded devices. Modifying the output layer and loss function allows CNNs to be utilized for regression tasks. Although CNNs are typically linked with classification tasks, they can also be employed to address regression problems involving continuous variable prediction or numeric value estimation. Tips and Tricks for Training Convolutional Neural Networks (CNNs) Training Convolutional Neural Networks (CNNs) effectively requires careful consideration of various factors and the application of specific algorithms and techniques. This section provides practical advice for training CNNs, including data augmentation, regularization algorithms, learning rate schedules, handling overfitting with algorithmic approaches, and improving generalization using advanced algorithms known as Hyperparameters.  Hyperparameters refer to the settings and configurations that are set before training the model. These parameters are not learned from the data but are manually defined by the user. Fine-tuning these hyperparameters can significantly impact the performance and training of the CNN model. Common metrics for training CNN models include accuracy, precision, recall, F1 score, and confusion metrics. It often requires experimentation and tuning to find the optimal combination of hyperparameters that results in the best model performance for a specific task or dataset. Another popular deep learning library, that provides a high-level interface for building and training neural networks, including CNNs is Keras. Data augmentation This is a powerful technique to expand the training dataset by applying random transformations to the existing data. It helps in reducing overfitting and improves model generalization. Common data augmentation techniques for images include random rotations, translations, flips, zooms, and brightness adjustments. Applying these transformations increases the diversity of the training data, enabling the model to learn robust and invariant features.  Regularization methods Regularization methods prevent overfitting and improve model generalization. Two widely used regularization techniques are L1 and L2 regularization, which add penalties to the loss function based on the magnitudes of the model's weights. This discourages large weight values and promotes simpler models. Another effective technique is dropout, which randomly sets a fraction of the neurons to zero during training, forcing the network to learn more robust and redundant representations. Learning Rate Schedules  Optimizing the learning rate is crucial for successful training. Gradually reducing the learning rate over time (learning rate decay) or decreasing it when the validation loss plateaus (learning rate scheduling) can help the model converge to better solutions. Techniques like step decay, exponential decay, or cyclical learning rates can be employed to find an optimal learning rate schedule for the specific task. Normalization In CNNs, normalization is an approach employed to preprocess input data and guarantee uniform scaling of features. Its purpose is to facilitate quicker convergence during training, prevent numerical instabilities, and enhance the network's overall stability and performance. Applications of Convolutional Neural Networks (CNNs) Convolutional Neural Networks (CNNs) have extended their impact beyond image classification, finding applications in various domains that require advanced visual analysis. This section highlights diverse applications of CNNs, such as object detection, semantic segmentation, and image generation.  💡 Notable use cases and success stories in different domains can be found here.  Object Detection  CNNs have revolutionized object detection by enabling the precise localization and classification of objects within an image. Techniques like region-based CNNs (R-CNN), Faster R-CNN, and You Only Look Once (YOLO) have significantly advanced the field. Object detection has numerous applications, including autonomous driving, surveillance systems, and medical imaging. Object Detection Semantic Segmentation  CNNs excel in semantic segmentation, where the goal is to assign a class label to each pixel in an image, enabling fine-grained understanding of object boundaries and detailed scene analysis. U-Net, Fully Convolutional Networks (FCN), and DeepLab are popular architectures for semantic segmentation. Applications range from medical image analysis and autonomous navigation to scene understanding in robotics. 💡 Interested in learning more about Semantic Segmentation? Read our Introduction to Semantic Segmentation.   Image Generation  CNNs have demonstrated remarkable capabilities in generating realistic and novel images. Generative Adversarial Networks (GANs) leverage CNN architectures to learn the underlying distribution of training images and generate new samples. StyleGAN, CycleGAN, and Pix2Pix are notable examples that have been used for image synthesis, style transfer, and data augmentation. CNNs excel in healthcare (diagnosis, patient outcomes), agriculture (crop health, disease detection), retail (product recognition, recommendations), security (surveillance, facial recognition), and entertainment (CGI, content recommendation). 💡Ready to bring your first machine learning model to life? Read How to Build Your First Machine Learning Model to kickstart your journey. Convolutional Neural Network (CNN): Key Takeaways Convolutional neural networks (CNNs) are a powerful tool for extracting meaningful features from visual data. CNNs are composed of a series of convolutional layers, pooling layers, and fully connected layers; convolutional layers apply filters to the input data to extract features, pooling layers reduce the size of the feature maps to prevent overfitting, and fully connected layers connect all neurons in one layer to all neurons in the next layer. CNNs can be used for a variety of tasks, including image classification, object detection, and semantic segmentation. Some notable CNN architectures include LeNet-5, AlexNet, VGGNet, and ResNet, InceptionNet, DenseNet, and MobileNet. CNNs are a rapidly evolving field, and new architectures and techniques are constantly being developed.

Jul 24 2023

sampleImage_llama2-explained

Llama 2: Meta AI's Latest Open Source Large Language Model

Meta AI and Microsoft have joined forces to introduce Llama 2, the next generation of Meta’s open-source large language model.  The best part? Llama 2 is available for free, both for research and commercial use. LLaMA: Large Language Model Meta AI Large Language Model Meta AI (LLaMA 1) is the first version of the state-of-the-art foundational large language model that was released by Meta in February this year. It is an impressive collection of foundational models, comprised of models with parameter sixes ranging from 7 billion to 65 billion. LLaMA 1 stands out due to its extensive training on trillion of tokens, showcasing that state-of-the-art models can be attained solely though publicly available datasets and without the need for proprietary or inaccessible data.  Notably, the LLaMA-13B model outperformed ChatGPT, which has a significantly larger parameter size of 175 billion, across most benchmarkdatasets. This accomplishment highlights LLaMA’s efficiency in delivering top-tier performance with significantly fewer parameters. The largest model of the collection, LLaMA-65B, holds its own amongst other leading models in the field of natural language processing (NLP) like Chinchilla-70B and PaLM-540B. LLaMA stands out due to its strong emphasis on openness and accessibility. Meta AI, the creators of LLaMA, have demonstrated their dedication to advancing the field of AI through collaborative efforts by releasing all their models to the research community. This is notably in contrast to OpenAI's GPT-3 or GPT-4.  💡 Read the published paper LLaMA: Open and Efficient Foundation Language Models.  Llama 2 Llama 2 is an updated collection of pre-trained and fine-tuned large language models (LLMs) introduced by Meta researchers. It encompasses models ranging from 7 billion to 70 billion parameters, each designed to deliver exceptional performance across various language processing tasks. Building upon its predecssor, LLaMA, LLaMA 2 brings several enhancements. The pretraining corpus size has been expanded by 40%, allowing the model to learn from a more extensive and diverse set of publicly available data. Additionally, the context length of Llama 2 has been doubled, enabling the model to consider a more extensive context when generating responses, leading to improved output quality and accuracy. Llama 2: Open Foundation and Fine-Tuned Chat Models One notable addition to Llama 2 is the adoption of grouped-query attention, which is expected to enhance attention and focus during language processing tasks. Llama 2-Chat is a version of Llama 2 that has been fine-tuned for dialogue-related applications. Through the fine-tuning process, the model has been optimized to deliver superior performance, ensuring it generates more contextually relevant responses during conversations. Llama 2 was pretrained using openly accessible online data sources. For the fine-tuned version, Llama 2-Chat, leveraged publicly available instruction datasets and used more than 1 million human annotations. 💡 Read the paper Llama 2: Open Foundation and Fine-Tuned Chat Models for more information on technical specifications. Across a range of external benchmarks, including reasoning, coding, proficiency, and knowledge tests, Llama 2 outshines other open-source language models. Llama 2: Open Foundation and Fine-Tuned Chat Models Meta researchers have released variants of Llama 2 and Llama 2-Chat with different parameter sizes, including 7 billion, 13 billion, and 70 billion. These variations cater to various computational requirements and application scenarios, allowing researchers and developers to choose the best-suited model for their specific tasks. This allows startups to access Llama 2 models to create their own machine learning products, including various generative AI applications or AI chatbots like Google’s Bard and OpenAI’s ChatGPT. 💡You can download the model here. Focus on Responsibility Meta's dedication to responsibility is evident in its open-source approach and emphasis on transparency. While recoginizing the profound societal advancements facilitated by AI, Meta remains aware of the associated risks. Their commitment to building responsibly is evident through several key initiatives undertaken during the development and release of Llama 2. Red-Teaming Exercises To ensure safety, Meta exposes fine-tuned models to red-teaming exercises. Through internal and external efforts, the models undergo thorough testing with adversarial prompts. This iterative process allows them to continuously enhance safety and address potential vulnerabilities, leading to the release of updated fine-tuned models based on these efforts. Transparency Schematic Meta promotes transparency by providing detailed insights into their fine-tuning and evaluation methods. Meta openly discloses known challenges and shortcomings, offering valuable information to the AI community. Their transparency schematic, found within the research paper, provides a roadmap of mitigations implemented and future explorations. Responsible Use Guide Meta acknowledges the importance of guiding developers in responsible AI deployment. To achieve this, they have developed a comprehensive Responsible User Guide. This resource equips developers with best practices for responsible development and safety evaluations, ensuring the ethical and appropriate use of Llama 2. Acceptable Use Policy Meta implemented an acceptable use policy to prevent misuse and safeguard against inappropriate usage. By explicitly defining certain prohibited use cases, Meta is actively promoting fairness and responsible AI application. Reinforcement Learning from Human Feedback Meta uses Reinforcement Learning from Human Feedback (RLHF) for Llama-2-chat to prioritize safety and helpfulness. This training technique used in Artificial Intelligence models improves model performance through interactions with human evaluators.  💡 Read the blog The Complete Guide to RLHF for Computer Vision for more information. A study of 180 samples revealed that the annotation platform choice affects downstream AI model performance. Model outputs were competitive with human annotations, suggesting prioritizing performance-based annotations for RLHF could enhance efficiency. Meta AI’s other recent releases Meta has achieved remarkable success with a series of open source tool releases in recent months. I-JEPA I-JEPA (Image-based Joint-Embedding Predictive Architecture) is a self-supervised learning approach for image representations. It efficiently learns semantic features without relying on hand-crafter data augmentations.  Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture Compared to pixel-reconstruction methods, I-JEPA excels in ImageNet-1K linear probing and low-level vision tasks such as object counting and depth prediction. With excellent scalability and efficiency, it outperforms previous pretraining approaches, making it a versatile and powerful tool for learning meaningful image representations. 💡Read the blog Meta AI’s I-JEPA, Image-based Joint-Embedding Predictive Architecture, Explained for more information. DINO v2 DINOv2 is a self-supervised learning method designed to acquire visual representations from images without the need for labeled data. Unlike transitional supervised learning approaches, DINOv2 overcomes the reliance on vast amounts of labeled data during training.  DINOv2: Learning Robust Visual Features without Supervision The DINOv2 workflow consists of two main stages: pre-training and fine-tuning. In the pretraining phase, the DINO model gains valuable visual insights by processing a vast collection of unlabeled images. Subsequently, during the fine-tuning stage, the pre-trained DINO model gets customized and adapted to handle specific tasks, such as image classification or object detection, using a dataset tailored to that task. 💡Read the blog DINOv2: Self-supervised Learning Model Explained for more information. Segment Anything Model (SAM) Segment Anything Model (SAM) revolutionizes image segmentation by adopting foundation models which are typically used in natural language processing (NLP).  Segment Anything SAM introduces prompt engineering, a novel approach that addresses diverse segmentation challenges. This empowers users to select objects for segmentation interactively, employing bounding boxes, key points, grids or text prompts. When faced with uncertainty regarding the object to be segmented, SAM exhibits the capability to generate multiple valid masks, enhancing the flexibility of the segmentation process. SAM has great potential to reduce labeling costs, providing a much-awaited solution for AI-assisted labeling, and improving labeling speedby orders of magnitude. 💡Read the blog Meta AI's New Breakthrough: Segment Anything Model (SAM) Explained for more information. ImageBind ImageBIND introduces an approach to learn a unified embedding space encompassing six diverse modalities: tex, image/video, audio, depth, thermal, and IMU. This technique enhances AI model’s ability to process and analyze data more comprehensively, incorporating information from various modalities, thus leading to a more humanistic understanding of the information at hand. ImageBind: One Embedding Space To Bind Them All To generate fixed-dimensional embeddings, the ImageBIND architecture employs separate encoders for each modality, coupled with linear projection heads tailored to individual modalities. The architecture primarily comprises three key components: Modality-specific encoders Cross-modal attention module Joint embedding space Although the framework’s precise specifications have not been made public, the research paper offers insights into the suggested architecture. 💡Read the blog ImageBind MultiJoint Embedding Model from Meta Explained for more information. Conclusion The introduction of Llama 2 represents a significant milestone. As an open-sourcelarge language model, Llama 2 offers boundless opportunities for research and commercial use, fueling innovation across various domains.

Jul 19 2023

Text2Cinemagraph: Synthesizing Artistic Cinemagraphs

Step into the captivating world of cinemagraphs, in which elements of a visual come to life with fluid motion. Crafting cinemagraphs has traditionally been a laborious process involving video capture, frame stabilization, and manual selection of animated and static regions. But what if there was a revolutionary method that brings cinemagraph creation to a whole new level of simplicity and creativity? Let’s delve into this exciting research. Introducing Text2Cinemagraph - this groundbreaking method leverages the concept of twin image synthesis to generate seamless and visually captivating cinemagraphs from user-provided text prompts. Text2Cinemagraph not only breathes life into realistic scenes but also allows creators to explore imaginative realms, weaving together various artistic styles and otherworldly visions. Text Prompt: ‘a large waterfall falling from hills during sunset in the style of Leonid Afremov’. Before diving into the details of Text2Cinemagraph, let’s discuss text-based synthetic cinemagraphs. Text-based Synthetic Cinemagraphs Cinemagraphs are visuals where certain elements exhibit continuous motion while the rest remain static. Traditionally, creating cinemagraphs has involved capturing videos or images with a camera and using semi-automated methods to produce seamless looping videos. This process requires considerable user effort and involves capturing suitable footage, stabilizing frames, selecting animated and static regions, and specifying motion directions.  Text-based cinemagraph synthesis expedites this process. The method generates cinemagraphs from a user-provided text prompt, allowing creators to specify various artistic styles and imaginative visual elements. The generated cinemagraphs can depict realistic scenes as well as creative or otherworldly compositions. There are two approaches for generating synthetic cinemagraphs: Text-to-Image Models One method is to generate an artistic image using a text-to-image model and subsequently animate it. However, this approach faces challenges as existing single-image animation techniques struggle  to predict meaningful motions for artistic inputs. This is primarily due to their training on real video datasets. Creating a large-scale dataset of artistic looping videos, however, is impractical due to the complexity of producing individual cinemagraphs and the wide variety of artistic styles involved. Text-to-Video Models An alternative approach is to use text-to-video models for generating synthetic cinemagraphs. Unlike the previous method of first generating an artistic image and then animating it, text-to-video models directly create videos based on the provided text prompts. However, experiments have revealed that these text-to-video methods often introduce noticeable temporal flickering artifacts in static regions and fail to produce the desired semi-periodic motions required for cinemagraphs. These issues arise due to the challenges of accurately predicting continuous and seamless motions solely from text descriptions. Text2Cinemagraph aims to overcome these limitations and enhance motion prediction by leveraging the concept of twin image synthesis. Text2Cinemagraph: Synthesizing Artistic Cinemagraphs from Text Text2Cinemagraph presents a fully-automated approach to generating cinemagraphs from text descriptions. The research paper, authored by Aniruddha Mahapatra and Jun-Yan Zhu from CMU and Aliaksandr Siarohin, Hsin-Ying Lee, and Sergey Tulyakov from Snap Research, introduces an innovative method that overcomes the difficulties of interpreting imaginary elements and artistic styles in the prompts to generate cinemagraphs. Synthesizing Artistic Cinemagraphs from Text Text2Cinemagraph achieves a seamless transfer of motion from a realistic image to an artistic one by synthesizing a pair of images. The process generates a visually appealing cinemagraph that brings the text description to life with fluid and mesmerizing motion. Text2Cinemagraph not only outperforms existing approaches but also offers extensions for animating existing paintings and controlling motion directions using text commands. Text Prompt: “a large river in a futuristic world, large buildings, cyberpunk style” Text2Cinemagraph: Core Design The core design of Text2Cinemagraph contains 3 elements: twin image generation, mask-guided optical flow prediction, and video generation. Synthesizing Artistic Cinemagraphs from Text Twin Image Generation The twin image generation method involves creating an artistic image from the input text prompt using Stable Diffusion and generating a corresponding realistic counterpart with a similar semantic layout. This is achieved by injecting self-attention maps and residual block features into the UNet module during the degeneration process, ensuring meaningful correspondence between the twin images.  This step lays the foundation for accurate motion prediction in the next step. Mask-Guided Flow Prediction Text2Cinemagraph uses a mask-guided approach to define the regions to animate in the image. The flow prediction model uses a pre-trained segmentation model which is trained on real images, ODISE, and user-specified region names, to predict the binary mask. The model is refined using self-attention maps from the diffusion model.  Using this mask as a guide, the flow prediction model generates the optical flow for the realistic image. The flow prediction model is conditioned not only on the mask but also on the CLIP embedding of the input text prompt. This allows the model to incorporate class information from the text, such as “waterfall” or “river,” to determine the natural direction in the predicted flow. This method effectively addresses the challenges of the boundaries of static regions and ensures smoother animation. Flow-Guided Video Generation After predicting the optical flow for the realistic image, it is transferred to animate the artistic image. This transfer is possible because of the similar semantic layout between the real and the artistic image.  Now, to generate the cinemagraph, each frame is generated separately. For the looping effect, the artistic image serves as both the first and last frame. Euler integration of the predicted optical flow is performed to obtain the cumulative flows in forward and backward directions.  Surprisingly, despite being trained on real-domain videos, the model can animate the artistic image without modification. This is achieved by essentially in painting small holes in the feature space generated during symmetric splatting, with surrounding textures providing repetitive patterns. Text2Cinemagraph: Results The training of Text2Cinemagraph involves two domains: Real Domain and Artistic Domain. The real domain includes a dataset of real-life videos with ground-truth optical flow, while the artistic domain generates artistic images from different captions generated using BLIP. 💡The PyTorch implementation of Text2Cinemagraph can be found here.  The models are trained with UNet backbones and cross-attention layers. Text2Cinemagraph outperforms recent methods in both domains, demonstrating its effectiveness in generating high-quality cinemagraphs from text prompts.  Real Domain Results Text prompt: ‘a large river flowing in front of a mountain in the style of starry nights painting’. In the real domain, Text2Cinemagraph outperforms baselines in both qualitative and quantitative evaluations. This method predicts more plausible flows, covering entire dynamic regions like rivers.  Synthesizing Artistic Cinemagraphs from Text Quantitatively, this method achieves significantly lower FVD scores, closely matching the fidelity of ground-truth videos. Hence, Text2Cinemagraph excels in generating high-quality cinemagraphs from real-world videos. Text Prompt: ‘Pirate ships in turbulent ocean, ancient photo, brown tint’. Artistic Domain Results Synthesizing Artistic Cinemagraphs from Text In the Artistic Domain, Text2Cinemagraph excels in qualitative comparison. It predicts cleaner flows, focusing accurately on desired regions, while baselines produce inaccurate flows with artifacts. Other text-to-video methods struggle to capture details or preserve temporal consistency. Text2Cinemagraph generates higher-quality cinemagraphs with smooth motion, overcoming limitations of other approaches and showcasing its ability to bring artistic visions to life. Text-Guided Direction Control This method also allows you to provide text-guided direction control for cinemagraph generation. This allows the manipulation of the direction of the movements in the cinemagraph according to the text prompt. Text Prompt: “a large river flowing in left to right, downwards direction in front of a mountain in the style of starry nights painting”. Text Prompt: “a large river flowing in upwards, right to left direction> in front of a mountain in the style of starry nights painting” Text2Cinemagraph: Limitations Text2Cinemagraph has some limitations: Artistic and realistic images may not always correspond to the input text, leading to missing dynamic regions in the generated images. Structural alterations in the artistic image can occur, even though it shares self-attention maps with the realistic image.  The pre-trained segmentation model (e.g., ODISE) might struggle with complex natural images, leading to imperfect segmentation and unusual movements in the generated cinemagraphs. Optical flow prediction may fail for images with unusual compositions and complex fluid dynamics. Significant changes in flow direction, like repeated zig-zag movement of water, may be challenging for the optical flow model to predict accurately. Text2Cinemagraph: Key Takeaways Text2Cinemagraph generates captivating cinemagraphs from text descriptions, offering a fully automated solution for cinemagraph creation. Concept behind Text2Cinemagraph: It uses twin image synthesis, transferring motion from realistic images to artistic ones for seamless animations. Text2Cinemagraph excels in generating high-quality cinemagraphs for real and artistic domains. It also enables text-guided direction control for manipulating movement based on text prompts. Read More Read more on other recent releases: Meta AI’s I-JEPA, Image-based Joint-Embedding Predictive Architecture, Explained MEGABYTE, Meta AI’s New Revolutionary Model Architecture Explained Meta Training Inference Accelerator (MTIA) Explained ImageBind MultiJoint Embedding Model from Meta Explained

Jul 18 2023

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F1 Score in Machine Learning

Machine learning (ML) has enabled companies to leverage large volumes of data to develop powerful models, generate insightful predictions, and make informed business decisions. But to ensure the quality of the ML pipeline, it is important to be able to conduct an in-depth evaluation of model performance. For this purpose, ML practitioners use evaluation metrics to determine the effectiveness of machine learning models. For instance, the F1 score is a fundamental metric for evaluating classification models and understanding how this metric works is crucial to ensure your classification model’s performance. In this article, we will dive into: The significance of evaluation metrics in machine learning The fundamentals of classification metrics Understanding & interpreting the F1 score metric ML applications where the F1 score metric is critical Limitations & caveats of F1 score metric F-score variants Model evaluation with Encord Active Evaluation Metrics in Machine Learning Evaluation metrics play a critical role in improving the accuracy, efficiency, quality, and effectiveness of machine learning models by providing objective and quantitative performance measures. For ML tasks, evaluation metrics: Provide model performance insights, i.e., data quality, correctness, error types, bias, and fairness. Assess the reliability and correctness of the model’s prediction Guide model selection by allowing a fair comparison of model variants Inform the hyperparameter tuning process Identify model limitations Aid stakeholders in decision-making Using multiple metrics to evaluate model performance is a common practice in ML tasks since a model can give good outcomes on one metric and perform suboptimally for another. In such cases, practitioners try to find a balance between various metrics. Different ML Tasks Have Different Evaluation Metrics ML tasks have unique objectives, and their corresponding models have distinct parameters and properties. Hence, there’s no one-size-fits-all approach when it comes to evaluating ML models for different tasks. For instance: Classification tasks require metrics like accuracy, precision, recall, F1 score, and AUC-ROC. Regression tasks employ metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. Clustering tasks are evaluated using metrics like the Silhouette score, Dunn index, and Rand index. Ranking & recommendation tasks use metrics like MAP, NDCG, and precision at K. 💡 Interested in learning about computer vision metrics? Here’s our Introductory Guide to Quality Metrics in Computer Vision. Before discussing the F1 score metric, let's understand the basics of classification metrics in more detail. Fundamentals of Classification Metrics Typically, classification tasks are categorized as binary classification (datasets with two classes or labels) and multi-class classification (datasets with more than two classes). Hence, classification models or classifiers try to predict labels or classes for the given data. Classification Prediction Outcomes Classifiers can have four possible outcomes: True Positives (TP): Events correctly predicted as positive. True Negatives (TN): Events accurately predicted as negative. False Positives (FP): Events wrongly predicted as positive when they were negative. False Negatives (FN): Events wrongly predicted as negative when they were positive. Most classification metrics such as accuracy, precision, recall (also known as sensitivity or true positive rate), specificity (true negative rate), F1 score (harmonic mean of precision and recall), and area under the ROC curve (AUC-ROC) use the above four outcomes for calculating metric values. Table 1: Sample outcomes of a binary classification model Confusion Matrix A confusion matrix is a useful tool to evaluate the performance of a classification model by mapping its actual and predicted values. In binary classification tasks, it is a table that shows the four prediction outcomes discussed above: true positives, true negatives, false positives, and false negatives. This two-dimensional matrix allows ML practitioners to summarize prediction outcomes in order to seamlessly calculate the model's precision, recall, F1 score, and other metrics. Consider the following confusion matrix as an example: Illustration of confusion matrix Understanding Accuracy, Precision & Recall Accuracy The accuracy metric calculates the overall prediction correctness by dividing the number of correctly predicted positive and negative events by the total number of events. The formula for calculating accuracy is Let’s use the data of the model outcomes from Table 1 to calculate the accuracy of a simple classification model: Typically, an accuracy score above 0.7 describes an average model performance, whereas a score above 0.9 indicates a good model. However, the relevance of the score is determined by the task. Accuracy alone may not provide a complete picture of model performance, especially In scenarios where class imbalance exists in the dataset. Therefore, to address the constraints of accuracy, precision, and recall metrics are used.  Precision The precision metric determines the quality of positive predictions by measuring their correctness. It is the number of true positive outcomes divided by the sum of true positive and false positive predictions. The formula applied in calculating precision is: Using the classification model outcomes from Table 1 above, precision is calculated as Precision can be thought of as a quality metric; higher precision indicates that an algorithm provides more relevant results than irrelevant ones. It is solely focused on the correctness of positive predictions, with no attention to the correct detection of negative predictions. Recall Recall, also called sensitivity, measures the model's ability to detect positive events correctly. It is the percentage of accurately predicted positive events out of all actual positive events. To calculate the recall of a classification model, the formula is Using the classification model outcomes from Table 1 above, recall is calculated as A high recall score indicates that the classifier predicts the majority of the relevant results correctly. However, the recall metric does not take into account the potential repercussions of false positives, i.e., occurrences that are wrongly identified as positive – a false alarm. Typically, we would like to avoid such cases, especially in mission-critical applications such as intrusion detection, where a non-malicious false alarm increases the workload of overburdened security teams. While precision and recall give useful information on their own, they also have limitations when viewed separately. Ideally, we want to build classifiers with high precision and recall. But that’s not always possible. A classifier with high recall may have low precision, meaning it captures the majority of positive classes but produces a considerable number of false positives. Hence, we use the F1 score metric to balance this precision-recall trade-off. F1 Score Metric The F1 score or F-measure is described as the harmonic mean of the precision and recall of a classification model. The two metrics contribute equally to the score, ensuring that the F1 metric correctly indicates the reliability of a model. It’s important to note that calculating the F1 score using arithmetic mean may not appropriately represent the model's overall performance, especially when precision and recall have considerably varied values. That’s because the arithmetic mean focuses on the sum of values and their average. On the other hand, the harmonic mean emphasizes the reciprocal of values. It is computed by dividing the total number of values by the sum of their reciprocals. Hence, it enhances the effect of the smaller value on the overall calculation to achieve a balanced measurement. As a result, the F1 score takes into account both precision-recall while avoiding the overestimation that the arithmetic mean might cause. The F1 score formula is  Using the classification model outcomes from Table 1, the F1 score is calculated as Here, you can observe that the harmonic mean of precision and recall creates a balanced measurement, i.e., the model's precision is not optimized at the price of recall, or vice versa. Hence, the F1 score shows a strong performance in recognizing positive cases while minimizing false positives and false negatives. This makes it a suitable metric when recall and precision must be optimized simultaneously, especially in imbalanced datasets. As a result, the F1 score metric directs real-world decision-making more accurately. Interpreting the F1 Score The F1 score ranges between 0 and 1, with 0 denoting the lowest possible result and 1 denoting a flawless result, meaning that the model accurately predicted each label. A high F1 score generally indicates a well-balanced performance, demonstrating that the model can concurrently attain high precision and high recall. A low F1 score often signifies a trade-off between recall and precision, implying that the model has trouble striking that balance. As a general rule of thumb, the F1 score value can be interpreted as follows: What is a good F1 score and how do I interpret it? However, depending on the task requirements, model use case, and the tolerance for mistakes, the precise threshold for what is considered “low” might also change. For instance, a simple decision tree classifier and a multi-layered deep learning neural network would have different ranges for high or low F1 scores. Now, let's consider various ML applications where model evaluation requires a balance of precision and recall, deeming the F1 score as a more suitable evaluation metric. ML Applications of F1 Score Medical Diagnostics In medical diagnostics, it is important to acquire a high recall while correctly detecting positive occurrences, even if doing so necessitates losing precision. For instance, the F1 score of a cancer detection classifier should minimize the possibility of false negatives, i.e., patients with malignant cancer, but the classifier wrongly predicts as benign. Sentiment Analysis For natural language processing (NLP) tasks like sentiment analysis, recognizing both positive and negative sentiments in textual data allow businesses to assess public opinion, consumer feedback, and brand sentiment. Hence, the F1 score allows for an efficient evaluation of sentiment analysis models by taking precision and recall into account when categorizing sentiments. Fraud Detection In fraud detection, by considering both precision (the accuracy with which fraudulent cases are discovered) and recall (the capacity to identify all instances of fraud), the F1 score enables practitioners to assess fraud detection models more accurately. For instance, the figure below shows the evaluation metrics for a credit card fraud detection model. Implementation of Credit Card Fraud Detection Using Random Forest Algorithm Limitations of F1 Score ML practitioners must be aware of the following limits and caveats of the F1 score when interpreting its results. Dataset Class Imbalance For imbalanced data, when one class significantly outweighs the other, the regular F1 scoremetric might not give a true picture of the model's performance. This is because the regular F1 score gives precision and recall equal weight, but in datasets with imbalances, achieving high precision or recall for the minority class may result in a lower F1 score due to the majority class's strong influence. 💡 Interested in learning more about class imbalance in datasets? Read our Introductory Blog on Balanced and Imbalanced Datasets in Machine Learning. Cost Associated with False Prediction Outcomes False positives and false negatives can have quite diverse outcomes depending on the application. In medical diagnostics, as discussed earlier, a false negative is more dangerous than a false positive. Hence, the F1 score must be interpreted carefully. Contextual Dependence The evaluation of the F1 score varies depending on the particular problem domain and task objectives. Various interpretations of what constitutes a high or low F1 score for different applications require various precision-recall criteria. Hence, a thorough understanding of the domain and the task at hand is needed to use and interpret the F1 score properly.  F-score Variants To resolve severe class imb