Review for NeurIPS paper: Learning Invariances in Neural Networks from Training Data

Authors propose to learn a method for data-augmentation that improves performance as compared to data-augmentation strategy where all parameters are randomized. The results are not on datasets used by SOTA methods and some of them are in the appendix instead of the main paper. I agree with the authors that R2 might have misunderstood the paper and did not participate in the post-rebuttal discussion. It is also mentioned in his review, "The remaining contribution of this paper is the use of the re-parametrization trick to adapt the group over which we want to be invariant on, which is in my opinion not a substantial contribution to present this paper in NeurIPS." I don't think we should judge papers solely based on novelty in the technical section.