From: NeurIPS.cc/2021/Workshop/DistShift/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: The authors observe that visual transformers due to their inductive bias tend to learn useful but non-robust features in that patch transformations that attack the semantics of the image do not really deteriorate performance classification performance. These features are likely not robust against distribution shifts. The paper hence proposes to actively use such patch-based-transformed images for negative augmentation (with different proposed losses) to improve robustness. Experiments show that there is minor improvement when using these negative augmentation losses on top of usual (semantic-preserving) augmentation procedures.