Deep Disentangled Metric Learning

Abstract

Proxy-based metric learning has enhanced semantic similarity with class representatives and exhibited noteworthy performance in deep metric learning (DML) tasks. While these methods alleviate computational demands by learning instance-to-class relationships rather than instance-to-instance relationships, they often limit features to be class-specific, thereby degrading generalization performance for unseen class. In this paper, we introduce a novel perspective called Disentangled Deep Metric Learning (DDML), grounded in the framework of information bottleneck, which applies class-agnostic regularization to existing DML methods. Unlike conventional NormSoftmax methods, which primarily emphasize distinct class-specific features, our DDML enables a diverse feature representation by seamlessly transitioning between class-specific features with the aid of class-agnostic features. It smooths decision boundaries, allowing unseen classes to have stable semantic representations in the embedding space. To achieve this, we learn disentangled representations of both class-specific and class-agnostic features in the context of DML. Empirical results demonstrate that our method addresses the limitations of conventional approaches. Our method easily integrates into existing proxy-based algorithms, consistently delivering improved performance.