Deep metric learning techniques have been used for visual representation in various supervised and unsupervised learning tasks through learning embeddings of samples with deep networks. However, classic approaches, which employ a fixed distance metric as a similarity function between two embeddings, may lead to suboptimal performance for capturing the complex data distribution. The Bregman divergence generalizes measures of various distance metrics and arises throughout many fields of deep metric learning. In this paper, we first show how deep metric learning loss can arise from the Bregman divergence. We then introduce a novel method for learning empirical Bregman divergence directly from data based on parameterizing the convex function underlying the Bregman divergence with a deep learning setting. We further experimentally show that our approach performs effectively on five popular public datasets compared to other SOTA deep metric learning methods, particularly for pattern recognition problems.

翻译：深度度量学习技术通过利用深度网络对样本进行嵌入学习，已广泛用于各类监督与无监督学习任务中的视觉表征。然而，传统方法采用固定距离度量作为两个嵌入之间的相似度函数，在捕捉复杂数据分布时可能导致性能次优。Bregman散度作为多种距离度量的泛化形式，贯穿于深度度量学习的诸多领域。本文首先揭示了深度度量学习损失如何源自Bregman散度，进而提出一种直接从数据中学习经验Bregman散度的新方法，其核心在于利用深度学习框架对Bregman散度所基于的凸函数进行参数化。实验结果表明，在五个公开基准数据集上，本方法相较于其他当前最优的深度度量学习方法（尤其是模式识别任务）展现出更优越的性能。