  In this paper, we propose a robust sample generation scheme to construct
informative triplets. The proposed hard sample generation is a two-stage
synthesis framework that produces hard samples through effective positive and
negative sample generators in two stages, respectively. The first stage
stretches the anchor-positive pairs with piecewise linear manipulation and
enhances the quality of generated samples by skillfully designing a conditional
generative adversarial network to lower the risk of mode collapse. The second
stage utilizes an adaptive reverse metric constraint to generate the final hard
samples. Extensive experiments on several benchmark datasets verify that our
method achieves superior performance than the existing hard-sample generation
algorithms. Besides, we also find that our proposed hard sample generation
method combining the existing triplet mining strategies can further boost the
deep metric learning performance.
