Venue: ICML 2021 Workshop AML

Reviews:

final_score: rating: Accept
review_content: The paper uses the Frank-Wolfe optimization to derive a relationship between loss landscape and attack distortion. Based on this relationship the authors propose an adaptive Frank-Wolfe adversarial training (FW-AT-Adapt) method to obtain robustness. This method can achieve robustness near or above multistep PGD-AT while significantly decreasing training time. Briefly, the paper provides a promising alternative to PGD-AT.

This work may shed light on future research on the connecting Frank-Wolfe optimization and adversarial robustness.
review_title: A promising alternative to PGD-AT

Meta-review: This paper proposed an adaptive Frank-Wolfe adversarial training method to improve robustness, achieving comparable robustness with PGD-AT while significantly decreasing training time.
Decision: Accept (Poster)