Deep Compositional Metric Learning

In this paper, we propose a deep compositional metric learning (DCML) framework for effective and generalizable similarity measurement between images. Conventional deep metric learning methods minimize a discriminative loss to enlarge interclass distances while suppressing intraclass variations, which might lead to inferior generalization performance since samples even from the same class may present diverse characteristics. This motivates the adoption of the ensemble technique to learn a number of sub-embeddings using different and diverse subtasks. However, most subtasks impose weaker or contradictory constraints, which essentially sacrifices the discrimination ability of each sub-embedding to improve the generalization ability of their combination. To achieve a better generalization ability without compromising, we propose to separate the sub-embeddings from direct supervisions from the subtasks and apply the losses on different composites of the sub-embeddings. We employ a set of learnable compositors to combine the sub-embeddings and use a self-reinforced loss to train the compositors, which serve as relays to distribute the diverse training signals to avoid destroying the discrimination ability. Experimental results on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate the superior performance of our framework.(1)

Automated summary

This paper proposes a deep compositional metric learning framework for effective and generalizable similarity measurement between images. By separating sub-embeddings from direct supervisions from the subtasks and applying losses on different composites of the sub-embeddings, the framework achieves better generalization ability without compromising. Employing learnable compositors to combine the sub-embeddings and using a self-reinforced loss to train the compositors, the framework distributes diverse training signals to avoid destroying the discrimination ability.