Last time, we talked about new use cases for synthetic data, from crowd counting to fractal-based synthetic images for pretraining large models. But there is a large set of use cases that we did not talk about, united by their relation to digital humans: human avatars, virtual try-on for clothes, machine learning for improving animations in synthetic humans, and much more. Today, we talk about the human side of CVPR 2022, considering two primary applications: conditional generation for applications such as virtual try-on and learning 3D avatars from 2D images (image generated by DALL-E-Mini by craiyon.com with the prompt “virtual human in the metaverse”).
Last time, we started a new series of posts: an overview of papers from CVPR 2022 that are related to synthetic data. This year’s CVPR has over 2000 accepted papers, and many of them touch upon our main topic on this blog. In today’s installment, we look at papers that make use of synthetic data to advance a number of different use cases in computer vision, along with a couple of very interesting and novel ideas that extend the applicability of synthetic data in new directions. We will even see some fractals as synthetic data! (image source)
CVPR 2022, the largest and most prestigious conference in computer vision and one of the most important ML venues in general, has just finished in New Orleans. With over 2000 accepted papers, reviewing the contributions of this year’s CVPR appears to be a truly gargantuan task. Over the next series of blog posts, we will attempt to go over the most interesting papers directly related to our main topic: synthetic data. Today, I present the first but definitely not the last installment devoted to papers from CVPR 2022.
After a long hiatus, we return from interviews to long forms, continuing (and hopefully finishing) our series on how synthetic data is used in machine learning and how machine learning models can adapt to using synthetic data. This is our seventh installment in the series (part 1, part 2, part 3, part 4, part 5, part 6), but, as usual, this post is (I hope!) sufficiently self-contained. We will discuss how one can have a model that works well on synthetic data without making it more realistic explicitly but doing the domain adaptation work at the level of features or model itself.
Hi all! Today we begin a new series of posts here in the Synthesis AI blog. We will talk to the best researchers and practitioners in the field of machine learning, discussing different topics but, obviously, trying to circle back to our main focus of synthetic data every once in a while.
Today we have our first guest, Professor Serge Belongie. He is a Professor of Computer Science at the University of Copenhagen (DIKU) and the Director of the Pioneer Centre for Artificial Intelligence. Previously he was the Andrew H. and Ann R. Tisch Professor at Cornell Tech and in the Computer Science Department at Cornell University, and an Associate Dean at Cornell Tech.
The role of synthetic data in developing solutions for autonomous driving is hard to understate. In a recent post, I already touched upon virtual outdoor environments for training autonomous driving agents, and this is a huge topic that we will no doubt return to later. But today, I want to talk about a much more specialized topic in the same field: driver safety monitoring. It turns out that synthetic data can help here as well—and today we will understand how. This is a companion post for our recent press release.
In a recent series of talks and related articles, one of the most prominent AI researchers Andrew Ng pointed to the elephant in the room of artificial intelligence: the data. It is a common saying in AI that “machine learning is 80% data and 20% models”, but in practice, the vast majority of effort from both researchers and practitioners concentrates on the model part rather than the data part of AI/ML. In this article, we consider this 80/20 split in slightly more detail and discuss one possible way to advance data-centric AI research.
In this (very) long post, we present an entire whitepaper on synthetic data, proving that synthetic data works even without complicated domain adaptation techniques in a wide variety of practical applications. We consider three specific problems, all related to human faces, show that synthetic data works for all three, and draw some other interesting and important conclusions.
It’s been a while since we last met on this blog. Today, we are having a brief interlude in the long series of posts on how to make machine learning models better with synthetic data (that’s a long and still unfinished series: Part I, Part II, Part III, Part IV, Part V, Part VI). I will give a brief overview of five primary fields where synthetic data can shine. You will see that most of them are related to computer vision, which is natural for synthetic data based on 3D models. Still, it makes sense to clarify where exactly synthetic data is already working well and where we expect synthetic data to shine in the nearest future.