  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.
