  Person re-identification is an important task and has widespread applications
in video surveillance for public security. In the past few years, deep learning
network with triplet loss has become popular for this problem. However, the
triplet loss usually suffers from poor local optimal and relies heavily on the
strategy of hard example mining. In this paper, we propose to address this
problem with a new deep metric learning method called Adversarial Triplet
Embedding (ATE), in which we simultaneously generate adversarial triplets and
discriminative feature embedding in an unified framework. In particular,
adversarial triplets are generated by introducing adversarial perturbations
into the training process. This adversarial game is converted into a minimax
problem so as to have an optimal solution from the theoretical view. Extensive
experiments on several benchmark datasets demonstrate the effectiveness of the
approach against the state-of-the-art literature.
