Review for NeurIPS paper: Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples

Additional Feedback: Post rebuttal I thank the authors for the response. I agree with all reviewers that the method is simple, effective, and generalized to improve GAN methods. However, I think the rebuttal does not address well all my concerns, some of them remain: 1. Regarding the novelty of the paper, using the D scores as the feedback to improve the generator quality is not new. What is new in the paper is the simple way how to use the discriminator scores. However, quite missing substantial discussion and comparison to related works to understand the advantages of the proposed method over the existing works, e.g., stronger improvements or better training time, etc? [*] Metropolis-Hastings Generative Adversarial Networks [**] Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling 2. The inconsistency of u value in the experiment is not addressed in the rebuttal.