  Person re-identification (re-id) is a cross-camera retrieval task which
establishes a correspondence between images of a person from multiple cameras.
Deep Learning methods have been successfully applied to this problem and have
achieved impressive results. However, these methods require a large amount of
labeled training data. Currently labeled datasets in person re-id are limited
in their scale and manual acquisition of such large-scale datasets from
surveillance cameras is a tedious and labor-intensive task. In this paper, we
propose a framework that performs intelligent data augmentation and assigns
partial smoothing label to generated data. Our approach first exploits the
clustering property of existing person re-id datasets to create groups of
similar objects that model cross-view variations. Each group is then used to
generate realistic images through adversarial training. Our aim is to emphasize
feature similarity between generated samples and the original samples. Finally,
we assign a non-uniform label distribution to the generated samples and define
a regularized loss function for training. The proposed approach tackles two
problems (1) how to efficiently use the generated data and (2) how to address
the over-smoothness problem found in current regularization methods. Extensive
experiments on four larges cale datasets show that our regularization method
significantly improves the Re-ID accuracy compared to existing methods.
