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学习样本嵌入，已在各类监督与无监督学习任务的视觉表示中得到了应用。然而，经典方法采用固定的距离度量作为两个嵌入之间的相似性函数，可能在捕捉复杂数据分布时导致性能次优。Bregman散度泛化了多种距离度量的测度，并广泛出现在深度度量学习的诸多领域。本文首先揭示了深度度量学习的损失函数如何源自Bregman散度。随后，我们提出了一种新颖方法，通过深度学习框架对Bregman散度背后的凸函数进行参数化，从而直接从数据中学习经验Bregman散度。进一步的实验表明，与当前最先进的深度度量学习方法相比，我们的方法在五个公开流行数据集上表现优异，尤其在模式识别问题中效果显著。