The mainstream researche in deep metric learning can be divided into two
genres: proxy-based and pair-based methods. Proxy-based methods have attracted
extensive attention due to the lower training complexity and fast network
convergence. However, these methods have limitations as the poxy optimization
is done by network, which makes it challenging for the proxy to accurately
represent the feature distrubtion of the real class of data. In this paper, we
propose a Calibrate Proxy (CP) structure, which uses the real sample
information to improve the similarity calculation in proxy-based loss and
introduces a calibration loss to constraint the proxy optimization towards the
center of the class features. At the same time, we set a small number of
proxies for each class to alleviate the impact of intra-class differences on
retrieval performance. The effectiveness of our method is evaluated by
extensive experiments on three public datasets and multiple synthetic
label-noise datasets. The results show that our approach can effectively
improve the performance of commonly used proxy-based losses on both regular and
noisy datasets.

本文提出了一种 Calibrate Proxy (CP) 结构，它使用真实样本信息提高了代理损失中的相似度计算，并引入校准损失来约束代理优化向类特征中心。 通过多个实验验证了我们方法在三个公共数据集和多个合成噪音标签数据集上的有效性，表明我们的方法可以有效地改善常用代理损失在正常和噪声数据集上的表现。