Review for NeurIPS paper: Exploiting weakly supervised visual patterns to learn from partial annotations

Weaknesses: - the method itself is pretty straightforward, but I wonder a bit at some choices. In particular, so much effort goes into computing many different distances (image-level/label-level and positive/negative), but almost all of this information is discarded because only the min value across all of these is used to compute the final temperature term. It just seems at odds with a "soft" penalty to add so many "hard" operations to throw away other potentially relevant information. Why not do some weighted combination? Were any other alternatives explored?