Review for NeurIPS paper: What Makes for Good Views for Contrastive Learning?

The paper studies contrastive methods for self-supervised representation learning. It studies how multiple views of the same data are used for representation learning, and how the mutual information between these views matters for downstream performance. The authors propose a theory that there is a sweet spot in the amount of mutual information between two views (not too less, not too much) such that the downstream performance is highest at this point. They empirically verify this theory for two classes of views (patches, and colors). They propose a method that simply combines existing augmentations from prior work and provides gains over them.