  As an effective way of metric learning, triplet loss has been widely used in
many deep learning tasks, including face recognition and person-ReID, leading
to many states of the arts. The main innovation of triplet loss is using
feature map to replace softmax in the classification task. Inspired by this
concept, we propose here a new adversarial modeling method by substituting the
classification loss of discriminator with triplet loss. Theoretical proof based
on IPM (Integral probability metric) demonstrates that such setting will help
the generator converge to the given distribution theoretically under some
conditions. Moreover, since triplet loss requires the generator to maximize
distance within a class, we justify tripletGAN is also helpful to prevent mode
collapse through both theory and experiment.
