深度度量学习综述

A brief introduction to deep metric learning

深度度量学习已成为近年来机器学习最具吸引力的研究领域之一,如何有效的度量物体间的相似性成为问题的关键。现有的依赖成对或成三元组的损失函数,由于正负样本可组合的数量极多,因此一种合理的解决方案是仅对训练有意义的正负样本采样,也称为“难例挖掘”。为减轻挖掘有意义样本时的计算复杂度,代理损失设置了数量远远小于样本集合的代理点集。该综述按照时间顺序,总结了深度度量学习领域比较有代表性的算法,并探讨了其与softmax分类的联系,发现两条看似平行的研究思路,实则背后有着一致的思想。进而文章探索了许多致力于提升softmax判别性能的改进算法,并将其引入到度量学习中,从而进一步缩小类内距离、扩大类间距,提高算法的判别性能。

Recently,deep metric learning(DML)has become one of the most attractive research areas in machine learn-ing.Learning an effective deep metric to measure the similarity between subjects is a key problem.As to existing loss functions that rely on pairwise or triplet-wise,as training data increases,and since the number of positive and negative samples that can be combined is extremely large,a reasonable solution is to sample only positive and negative samples that are meaningful for training,also known as Difficult Case Mining.To alleviate computational complexity of mining meaningful samples,the proxy loss chooses proxy sets that are much smaller than the sample sets.This review summar-izes some algorithms representative of DML,according to the time order,and discusses their relationship with softmax classification.It was found that these two seemingly parallel research methods have a consistent idea behind them.This paper explores some improved algorithms that aim to improve the softmax discriminative performance,and introduces them into metric learning,so as to further reduce intra-class distance,expand inter-class distance,and,finally,improve the discriminant performance of the algorithm.