Deep Asymmetric Metric Learning Via Rich Relationship Mining

Learning effective distance metric between data has gained increasing popularity, for its promising performance on various tasks, such as face verification, zero-shot learning, and image retrieval. A major line of researches employs hard data mining, which makes efforts on searching a subset of significant data. However, hard data mining based approaches only rely on a small percentage of data, which is apt to overfitting. This motivates us to propose a novel framework, named deep asymmetric metric learning via rich relationship mining (DAMLRRM), to mine rich relationship under satisfying sampling size. DAMLRRM constructs two asymmetric data streams that are differently structured and of unequal length. The asymmetric structure enables the two data streams to interlace each other, which allows for the informative comparison between new data pairs over iterations. To improve the generalization ability, we further relax the constraint on the intra-class relationship. Rather than greedily connecting all possible positive pairs, DAMLRRM builds a minimum-cost spanning tree within each category to ensure the formation of a connected region. As such there exists at least one direct or indirect path between arbitrary positive pairs to bridge intra-class relevance. Extensive experimental results on three benchmark datasets including CUB-200-2011, Cars 196, and Stanford Online Products show that DAMLRRM effectively boosts the performance of existing deep metric learning approaches.