NeurIPS 2020

Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness

Meta Review

The paper was extensively discussed among the reviewers. The final outcome was that all the reviewers agreed that the theoretical part of the paper is not significantly novel and the authors have to rewrite that part (please see the updated reviews), however, the approach is novel and experimental part is strong. To evaluate the experimental part further, a new reviewer was added after the rebuttal who has a good understanding on the experimental side of the topic of adversarial data augmentation. The new reviewer confirmed that the usefulness of the entropy-based regularization term toward providing robustness against unseen shifts is significant.