Review for NeurIPS paper: HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss

Weaknesses: - Contributions that are claimed are somewhat weak. In fact, I think that main contribution is in the gradient analysis (as I mentioned in Strengths) and all of the three claimed contributions can be bundled into one "incremental improvements of previous works". Namely: C1: L2-regularization is interesting, but ablation study shows it has the smallest effect on the performance; C2: hybrid similarity measure is a simple combination of two established similarity measures, and it additionally adds another hyper-parameter (alpha) that seems to be very sensitive to setup (Fig5(a) shows that setting lower alpha reduces performance by 1mAP, and setting higher reduces by 0.5 mAP); C3: novel architecture is actually almost identical architecture as [21,22] with addition of FRN block from [36] (ablation study shows this gives the most increase to performance), so it more of a practical combination of previous work than actual contribution.