Adversarial robustness as a prior for better transfer learning - Microsoft Research

Editor's note: This post and its research are the collaborative efforts of our team, which includes Andrew Ilyas (PhD Student, MIT), Logan Engstrom (PhD Student, MIT), Aleksander Mądry (Professor at MIT), Ashish Kapoor (Partner Research Manager). In practical machine learning, it is desirable to be able to transfer learned knowledge from some "source" task to downstream "target" tasks. This is known as transfer learning--a simple and efficient way to obtain performant machine learning models, especially when there is little training data or compute available for solving the target task. Transfer learning is very useful in practice. For example, transfer learning allows perception models on a robot or other autonomous system to be trained on a synthetic dataset generated via a high-fidelity simulator, such as AirSim, and then refined on a small dataset collected in the real world.