Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: This paper proposed achieving representational fairness by using group-wise gradient norm clipping to control gradient groups’ magnitude during discriminator training
strengths_and_weaknesses: **Weaknesses**
1. The datasets used in this paper is not for fairness purpose. I recommend using datasets for fairness.
2. The experiment only explores the behavior of the gradient, which does not report the fairness performance.

**Strengths**
1. Analyzing the behavior of the gradient for fairness is interesting and important.

suggested_changes: 1. Add datasets for fairness
2. Evaluate the fairness performance.
review_title: null

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: The paper proposes a new scheme for training GANs to mitigate representation bias through group-wise gradient norm clipping. The experiments show that the proposed method is effective in average gradient (fig. 2), frequency (fig. 3) and distributions (fig. 5 & 6).
strengths_and_weaknesses: Strengths:
1. The topic that this work focuses on is interesting and the proposed RepFair-GAN seems effective in that regard.
2. The idea of RepFair-GAN is illustrated clearly with well-written descriptions and experiments.

Weakness:
1. Although claimed to be task-agnostic, experiments of downstream tasks performance comparisons between RepFair-GAN and vanilla GANs would be meaningful to demonstrate the usefulness of RepFair-GAN.
suggested_changes: Although the author(s) argue that the proposed work does not target a specific task for classification, it should be understood as such scheme can improve multiple downstream tasks. Therefore, it would be still meaningful to conduct further experiments and analysis on if RepFair-GAN would help achieve better task results than other baseline GANs.
review_title: null

Decision: Invite to revise