From: NeurIPS.cc/2022/Workshop/HITY/Program_Chairs
title: Paper Decision
decision: Accept

From: NeurIPS.cc/2022/Workshop/HITY/Paper7/Reviewer_EqEf
title: Accept: Combining sharpness-aware minimization with self-distillation seems to improve performance
review: The paper proposes to combine sharpness-aware minimization with self-distillation to speed up convergence and improve test performance. Since the empirical results indicate a potential improvement in performance, I recommend to accept the paper.

Some simple suggestions to improve the paper:
- Provide results averaged over multiple random seeds.
- Improve the readability of the figures and the table by increasing the font size.
- Improve the clarity of the method section (Section 3).

rating: 1: accept
confidence: 2: The reviewer is willing to defend the evaluation, but it is quite likely that the reviewer did not understand central parts of the paper

From: NeurIPS.cc/2022/Workshop/HITY/Paper7/Reviewer_wD6Q
title: Review
review: This paper combines recent methods in improving NNs' generalization performance: SAM and self-distillation. The initial experiments results look good and the method is interesting. Thus, I vote for acceptance.
rating: 1: accept
confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct

From: NeurIPS.cc/2022/Workshop/HITY/Paper7/Reviewer_fpV5
title: SADT combines SAM and self-distillation and outperforms them in experiments
review: This paper combines sharpness-aware minimization and self-distillation in one method called SADT. The presentation is reasonably clear. The experiments are impressive because SADT outperforms SAM, which is sota.  If the results are not cherry picked, this is a very important contribution -- which I would like to see as a follow up conference paper. Accept!

Please fix though: The legend in Figure 2 is much too small. 
rating: 1: accept
confidence: 2: The reviewer is willing to defend the evaluation, but it is quite likely that the reviewer did not understand central parts of the paper