2022: My Top 10 Picks for Educational Content on Synthetic Data for Computer Vision
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2022 was a magnificent year for synthetic data within the computer vision domain.
This field, which involves creating artificial datasets to enable and improve perception algorithms, has seen incredible progress and innovation.
More datasets. More tools. More vendors. More research. More robust implementations in production.
Synthetic data is driving computer vision & AI adoption and enabling perception research.
There has been a surge in new educational content on various aspects of synthetic data generation, attracting new engineers, scientists, and most importantly, adopters.
In the past year, I have read and watched nearly all of the educational content on synthetic data generation for computer vision. And in this week's newsletter, I would like to share my favorite 10 pieces of content covering different aspects of this fascinating field that will hopefully help you on your learning journey.
Fake It Till You Make It: Face Analysis in the Wild Using Synthetic Data Alone
Matthew Johnson from Microsoft Mixed Reality lab discusses their research on understanding accurate human face movement without markers. The team has developed a comprehensive synthetic dataset of diverse human faces. In this presentation, Johnson explains the process of creation of the dataset and how they balanced the competing goals of synthetic data —?realism, diversity, and label richness.
The Future of Robots & Perception Systems with Synthetic Data
Ekaterina Sirazitdinova , a data scientist at NVIDIA, presents on the topic of synthetic data and its use in enabling robots and other embedded devices. This presentation discusses the challenges of automated robots and the need for fully autonomous robots that can act and react to their environments on their own. The speaker emphasizes the importance of data in training AI robots and presents the use of synthetic data as a solution to the problem of limited real-world data. They also discuss the benefits of synthetic data, including its ability to be easily manipulated and controlled, and its potential to improve the performance of AI models.
Using Unity to Generate Synthetic Data and Accelerate Computer Vision Training
In this seminar, James Fort and Jonathan Hogins discuss how to generate synthetic data for computer vision applications using Unity Perception SDK. They highlight the importance of computer vision in various industries, such as autonomous vehicles, robotics, retail, and security. They also discuss the challenges of collecting and labeling data for machine learning and deep learning processes, as well as the issue of bias and insufficiency in real-world data. They then propose using synthetic data to overcome these challenges and provide examples of how this can be done. The speakers introduce Unity's Perception SDK, which enables the development of high-quality computer vision models by generating synthetic data.
Creating?High-Quality Datasets for Training Machine Learning Models With SageMaker Ground Truth
The seminar covers creating high-quality datasets for training machine learning models through data labeling and synthetic data generation services within AWS SageMaker Ground Truth. The speakers discuss the challenges faced in data labeling projects, such as time and cost efficiency and label accuracy. They also introduce different AWS services, such as SageMaker Ground Truth and Ground Truth Plus, that help overcome these challenges and improve the data labeling process.
Next-Generation?Tools?for?Synthetic?Data?and?AI?Training
In the presentation, Nathan Kundtz , CEO of Rendered.ai , talks about next-generation tools for synthetic data and AI training. He discusses how synthetic data is different from pure simulations, the architecture required to effectively generate synthetic data, and how a platform approach can address these needs. He also provides a case study and a demo of synthetic data generation in action. Nathan emphasizes the importance of data in AI and the challenges of collecting, cleaning, and refining datasets. He argues that synthetic data can help overcome these challenges by providing rare and annotated data for AI training.
If you are interested in further details, Rendered.ai has another more technical overview of their platform .
Synthetic Data with Yashar Behzadi, CEO/Founder of Synthesis AI —?Data Exchange Podcast (Episode 146)
In this podcast, Yashar Behzadi , CEO and founder of Synthesis AI , discusses the use of synthetic data in computer vision. Yashar explains how synthetic data can help alleviate the problems associated with acquiring, labeling, and preparing real-world data for machine learning. He argues that synthetic data enables the development of computer vision products in a different way and can solve many fundamental issues. Yashar also predicts that within three years, startups will be able to train object recognition models for autonomous vehicles solely on synthetic data.
In this episode of the TWIML AI Podcast, host Sam Charrington interviews Bill Vass , VP of Engineering at Amazon Web Services. The two discuss synthetic data and its role in ML. The conversation focuses on the use of synthetic data in machine learning for robotics and its benefits and challenges. Vass discusses how synthetic data can be used to augment real data, and the importance of ensuring it is representative of the real world. This podcast is filled with real-world examples and references to different Amazon products enabled by synthetic data.
Interview with Ofir Chakon, CEO and Co-founder, Datagen
The CEO and co-founder of Datagen , Ofir Zuk (Chakon) , talks about his passion for AI and the work that his company is doing in creating synthetic data for AI models. Ofir talks about the early days of Datagen with his partner and CTO Gil Elbaz and shares his thoughts on the potential of synthetic data for computer vision.
Although the next 2 videos were published at the end of 2021, I'm still including those here because I believe they are highly relevant and contribute significantly to this list.
How Toyota Research Institute Trains Better Computer Vision Models with Parallel Domain Synthetic Data
In this interview, Kevin McNamara and Adrien Gaidon discuss synthetic data's advantages in machine learning research. McNamara is the CEO of Parallel Domain , a synthetic data generation platform, and Gaidon is the head of machine learning research at Toyota Research Institute (TRI). They discuss the benefits of synthetic data (it is safer, programmable, and fast) and how it allows for more control over data generation and the ability to engineer the data sets, which can be useful for testing new model types. TRI has used synthetic data to train models for self-driving cars and robots and has seen significant improvements in performance compared to using real-world data alone. The use of synthetic data allows for faster deployment of models into the real world.
The Promising Role of Synthetic Data To Enable Responsible Innovation
In this presentation, Shalini Kurapati, PhD , the co-founder and CEO of Clearbox AI , a synthetic data startup, discusses synthetic data and its applications with a focus on responsible innovation. The speaker discusses the potential benefits of synthetic data, as well as potential ethical concerns around bias and fairness. Although the use cases provided in this presentation relate to structured/tabular synthetic data, the concepts of bias and fairness translate well into the computer vision domain.
Do you have other examples of great educational content on synthetic data for computer vision? Please share in the comments.
And now, let's dive into the news headlines of recent days!
How It Feels To Be Sexually Objectified by an AI
Lensa, an AI avatar app, has been found to produce sexist, racist, and sexualized results when used by an Asian woman. It uses the Stable Diffusion AI model to generate images and is trained on the LAION-5B open-source data set that is compiled from images scraped from the internet. This dataset appears to include pictures reflecting sexist, racist stereotypes, therefore, promoting those biases in the content generated by these generative models.
Stable Diffusion 2.0-2.1 Prompt Book Is Out!
Stability AI has just published a visual guide to exploring prompts for generating images using highly popular Stable Diffusion 2.0 and 2.1 generative models.
You Can Now Create High-Resolution 3D Meshes With Text Prompts
Luma AI , a company behind a popular 3D model generation service based on photos, has just announced a text-to-3D service allowing the creation of high-resolution 3D meshes with text prompts.
Not All Synthetic Datasets Are Created Equal
Parallel Domain , a synthetic data generation provider, improves unsupervised domain adaptation performance by 30% vs. GTA with no changes to the model architecture.
Meet Rokoko Video:?A Free Browser-based AI Motion Capture Tool
Rokoko , a Danish company known for its motion capture suits and gloves, launched the Rokoko video — a free browser-based tool for motion capture from a web camera.
Five Actionable Tips To Start Using Generative AI in Client Briefs
Pinar Demirdag , a co-founder of the creative agency Seyhan Lee , is sharing strategies on?how to ideate for client briefs by using generative A.I. to help creatives working with brands think of new ideas.
China’s Generative AI Rules Set Boundaries and Punishments for Misuse
The Cyberspace Administration of China (CAC) has?issued regulations?that ban the creation of any artificial intelligence-generated media, including deepfakes, that aren't clearly labeled.
Synthetic Media Companies Still Hiring
And that's a wrap!
Here are a few more ways you can learn about synthetic data and generative AI:
See you next week!
AI/LLM Disruptive Leader | GenAI Tech Lab
1 年See aso my new book "Synthetic Data", at https://mltblog.com/3XCsVw9. Below is an extract.
Data Science | AI, ML, Semantic Knowledge Graphs, Computer Vision
1 年Andrey Shtylenko do you ever find that synth overfits models once you begin testing in target environment?
Product Content Creator
1 年I think Sam Charrington just did a podcast with an AI bot as the guest. I have not listened yet, but it sounds very interesting.
What a great list! Thank you for sharing and including Ofir Zuk (Chakon). Looking forward to 2023!
AI & ML Specialization, xCTO & founder of Datagen
1 年Great Picks Andrey Shtylenko! Always a pleasure to get your curated content lists ??