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In this paper, we propose a robust sample generation scheme to constructinformative triplets. The proposed hard sample generation is a two-stagesynthesis framework that produces hard samples through effective positive andnegative sample generators in two stages, respectively. The first stagestretches the anchor-positive pairs with piecewise linear manipulation andenhances the quality of generated samples by skillfully designing a conditionalgenerative adversarial network to lower the risk of mode collapse. The secondstage utilizes an adaptive reverse metric constraint to generate the final hardsamples. Extensive experiments on several benchmark datasets verify that ourmethod achieves superior performance than the existing hard-sample generationalgorithms. Besides, we also find that our proposed hard sample generationmethod combining the existing triplet mining strategies can further boost thedeep metric learning performance.

Construct Informative Triplet with Two-stage Hard-sample Generation