We consider the problem of distance metric learning (DML), where the task is
to learn an effective similarity measure between images. We revisit ProxyNCA
and incorporate several enhancements. We find that low temperature scaling is a
performance-critical component and explain why it works. Besides, we also
discover that Global Max Pooling works better in general when compared to
Global Average Pooling. Additionally, our proposed fast moving proxies also
addresses small gradient issue of proxies, and this component synergizes well
with low temperature scaling and Global Max Pooling. Our enhanced model, called
ProxyNCA++, achieves a 22.9 percentage point average improvement of Recall@1
across four different zero-shot retrieval datasets compared to the original
ProxyNCA algorithm. Furthermore, we achieve state-of-the-art results on the
CUB200, Cars196, Sop, and InShop datasets, achieving Recall@1 scores of 72.2,
90.1, 81.4, and 90.9, respectively.

通过对 ProxyNCA 进行改进和优化，我们提出了 ProxyNCA++ 这一性能更卓越的模型，并在零样本检索数据集上实现了 22.9% 的平均召回率提升，并在 CUB200、Cars196、Sop 和 InShop 数据集上取得了最佳结果。