Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies

Deep metric learning plays a key role in various machine learning tasks. Most of the previous works have been confined to sampling from a mini-batch, which cannot precisely characterize the global geometry of the embedding space. Although researchers have developed proxy-and classification-based methods to tackle the sampling issue, those methods inevitably incur a redundant computational cost.