example, a 1. 2% reduction in error on CIFAR100 (without synthetic noise) simply by removing data

We thank the reviewers for their helpful feedback. We are encouraged that you note AUM's simplicity--"works with It seems that R3, as they admit themself, is "confused" by our submission and contribution. We could cite [Wang et al., CVPR 2018] (as suggested by R3) but Additionally, we clearly discuss/compare to Co-Teaching in Sec. However, we do agree with R3's point concerning the subsampled Clothing1M dataset (see response to R4). Thank you for your supportive comments and interesting remarks. Thus the difference between AUM and standard training is 0. 2%. Thank you for positive feedback and detailed questions. We hope to address them here and in the camera ready. "Do the removed samples introduce new problem?" WebVision are less likely to be mislabeled (e.g. We will discuss this more in Sec. 5. "How to choose a good set of [threshold] samples?": We choose We are unclear what you mean by "the assigned logit "Analyses about the difference AUM and original margin": AUM is more robust and consistent than the margin Averaging across epochs increases the "signal to noise ratio."