Venue: AAAI 2022 Workshop AdvML

Reviews:

final_score: rating: 7: Good paper, accept
review_content: This paper proposes an interesting approach for adversarial training, that the data augmentation is applied to the output space.

Advantages:
- Figure 1 is clear to show the motivation of this paper, while is also reasonable.
- The proposed technique is simple and general for various adversarial training paradigms, including supervised learning, self-supervised learning.
- The results are clear to show the effectiveness.

I have some suggestions to further improve this paper:
- The section number is missing in the paper, e.g., in the first paragraph in Method, "Multiple approaches for adversarial training are outlined in Section ."
- The authors are encouraged to conduct experiments using AutoAttack.

Overall, this is a good paper with sufficient contribution to the field. Thus I recommend acceptance.
review_title: Review for the paper "Broad Adversarial Training with Data Augmentation in the Output Space"

Meta-review: The reviewer agrees to accept this paper. Please consider the suggestions in the camera-ready version.
Decision: Accept (Long Paper)