Adversarial Robustness on In- and Out-Distribution Improves Explainability

Code

• github.com/M4xim4l/InNOutRobustness
  Officialpytorch★ 12

Abstract

Neural networks have led to major improvements in image classification but suffer from being non-robust to adversarial changes, unreliable uncertainty estimates on out-distribution samples and their inscrutable black-box decisions. In this work we propose RATIO, a training procedure for Robustness via Adversarial Training on In- and Out-distribution, which leads to robust models with reliable and robust confidence estimates on the out-distribution. RATIO has similar generative properties to adversarial training so that visual counterfactuals produce class specific features. While adversarial training comes at the price of lower clean accuracy, RATIO achieves state-of-the-art l_2-adversarial robustness on CIFAR10 and maintains better clean accuracy.

Tasks

Reproductions