Author: @snikolenko

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 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.