Among various supervised deep metric learning methods proxy-based approaches
have achieved high retrieval accuracies. Proxies, which are
class-representative points in an embedding space, receive updates based on
proxy-sample similarities in a similar manner to sample representations. In
existing methods, a relatively small number of samples can produce large
gradient magnitudes (ie, hard samples), and a relatively large number of
samples can produce small gradient magnitudes (ie, easy samples); these can
play a major part in updates. Assuming that acquiring too much sensitivity to
such extreme sets of samples would deteriorate the generalizability of a
method, we propose a novel proxy-based method called Informative Sample-Aware
Proxy (Proxy-ISA), which directly modifies a gradient weighting factor for each
sample using a scheduled threshold function, so that the model is more
sensitive to the informative samples. Extensive experiments on the
CUB-200-2011, Cars-196, Stanford Online Products and In-shop Clothes Retrieval
datasets demonstrate the superiority of Proxy-ISA compared with the
state-of-the-art methods.

本研究提出了一种名为 Informative Sample-Aware Proxy（Proxy-ISA）的基于代理（proxy）的深度度量学习方法，该方法使用一种定时的阈值函数直接修改每个样本的梯度权重因子，使模型更加敏感于信息样本，进而取得更优的检索精度表现。