Review for NeurIPS paper: Deep Metric Learning with Spherical Embedding

This paper points out a widespread problem with angular losses, and proposes a simple, elegant scheme to address the problem (regularizing each embedding to lie on a shell), getting moderate but consistent improvements across a range of problem settings and datasets. As pointed out by Reviewer 5, the majority of the theoretical results were already known in Section 3.3 of "Heated-Up Softmax Embedding" (2018, unpublished, https://arxiv.org/abs/1809.04157). That paper, however, did not really propose a solution to the problem, merely noted its existence. Reviewer 5 also complains that the interaction with the Adam optimizer is under-explored in this work. "Improved Deep Metric Learning with Multi-class N-pair Loss Objective," also regularized the L2 norm of embedding vectors (towards 0; see their Section 3.2.2).