NeurIPS 2020

Debiased Contrastive Learning

Meta Review

Reviewers unanimously agreed that this submission should be accepted -- it's well-written and proposes an interesting, well-motivated, and theoretically grounded idea. It's cheap and easy to implement and add to any existing contrastive learning approach, and the empirical evaluation showed strong results. Unsupervised contrastive learning is currently an area with plenty of interest among the NeurIPS audience so it is likely to be relevant/interesting to a large slice of the community. The authors' rebuttal addressed most of the reviewers' initial concerns. The paper could be further strengthened by evaluating the method in the full ImageNet-1K setting as this is the standard benchmark for most unsupervised visual representation learning work (even if the bias isn't large enough for the method to have much effect with 1K classes, this is useful for readers to know) -- the authors should strongly consider including these results in the camera-ready version of the paper.