Venue: ML_Reproducibility_Challenge 2021 Fall

Reviews:

final_score: rating: 7: Good paper, accept
review_content: The authors clearly state the claims and design the corresponding experiments. In order to perform the experiments in low-computation-resources situations, they re-implement the code to use a smaller model and dataset. The reproduction shows the efficacy in a different setting from the original paper.

The authors reproduce the discrepancy given the smaller dataset in traditional adversarial training, and show the efficacy after implementing FRL. The exploration process is complete.

The authors find deviations from the original paper in certain cases. The results provide additional insights.
review_title: Accept

Meta-review: Following the recommendation of reviewers and meta-reviewer, the paper is accepted for ML Reproducibility Challenge 2021, and will be published in the upcoming special edition of ReScience Journal.

Additionally, after several rounds of discussion and incorporating recommendations from the Area Chairs and Program Chairs, the report has been granted an **Outstanding Paper Award** due to its exceptional quality of all-round reproducibility effort. Congratulations!
Decision: Accept (Outstanding Paper)