From: ICML.cc/2021/Workshop/AML/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: This paper proposed an adversarial purification method for defense. The reviewer agreed that the paper is solid and the experiments are extensive.

From: ICML.cc/2021/Workshop/AML/Paper61/Reviewer_hqFc
title: The paper proposed AID-purifier, which is a light auxiliary network and can boost the robustness of adversarially-trained networks by purifying their inputs.
review: The paper considered adversarial purification for adversarial defense and proposed a computationally light and easily attachable purifier: AID-Purifier, which is the first successful purification method that is based on a simple discriminator. AID-Purifier trains its discriminator with the BCE loss between $x_{adv}$ and $x_{clean}$ with AVmixup and inference with iterative purifification. Extensive experiments show AID-Purifier can improve the robustness of the model as well as decreasing the inference time significantly. The method in this paper is effective and the experiment is solid.
rating: Accept
confidence: 3: The reviewer is fairly confident that the evaluation is correct