  Existing metric learning losses can be categorized into two classes:
pair-based and proxy-based losses. The former class can leverage fine-grained
semantic relations between data points, but slows convergence in general due to
its high training complexity. In contrast, the latter class enables fast and
reliable convergence, but cannot consider the rich data-to-data relations. This
paper presents a new proxy-based loss that takes advantages of both pair- and
proxy-based methods and overcomes their limitations. Thanks to the use of
proxies, our loss boosts the speed of convergence and is robust against noisy
labels and outliers. At the same time, it allows embedding vectors of data to
interact with each other in its gradients to exploit data-to-data relations.
Our method is evaluated on four public benchmarks, where a standard network
trained with our loss achieves state-of-the-art performance and most quickly
converges.
