NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:4658
Title:Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks

This paper proposes the use of error correcting codes as class representations to improve robustness for adversarial attacks. The main idea of error correcting output codes is well-known, but this is the paper that shows that such ideas can be used for adversarial robustness. The paper shows very promising results especially in the rebuttal for CIFAR10. The distance bound is equivalent to Plotkin as the reviewer pointed out so this should be fixed in the paper.