Learnable dynamic margin in deep metric learning

Abstract

With the deepening of deep neural network research, deep metric learning has been further developed and achieved good results in many computer vision tasks. Deep metric learning trains the deep neural network by designing appropriate loss functions, and the deep neural network projects the training samples into an embedding space, where similar samples are very close, while dissimilar samples are far away. In the past two years, the proxy-based loss achieves remarkable improvements, boosts the speed of convergence and is robust against noisy labels and outliers due to the introduction of proxies. In the previous proxy-based losses, fixed margins were used to achieve the goal of metric learning, but the intra-class variance of fine-grained images were not fully considered. In this paper, a new proxy-based loss is proposed, which aims to set a learnable margin for each class, so that the intra-class variance can be better maintained in the final embedding space. Moreover, we also add a loss between proxies, so as to improve the discrimination between classes and further maintain the intra-class distribution. Our method is evaluated on fine-grained image retrieval, person re-identification and remote sensing image retrieval common benchmarks. The standard network trained by our loss achieves state-of-the-art performance. Thus, the possibility of extending our method to different fields of pattern recognition is confirmed.