Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks

This paper proposes a fundamentally different approach to adversarial defense which requires essentially no architectural changes to existing discriminative learning pipelines, is wholly complementary to other defense approaches including adversarial training, and does not sacrifice benign test set performance.