  While recent person re-identification (ReID) methods achieve high accuracy in
a supervised setting, their generalization to an unlabelled domain is still an
open problem. In this paper, we introduce a novel unsupervised disentanglement
generative adversarial network (UD-GAN) to address the domain adaptation issue
of supervised person ReID. Our framework jointly trains a ReID network for
discriminative features extraction in a source labelled domain using identity
annotation, and adapts the ReID model to an unlabelled target domain by
learning disentangled latent representations on the domain. Identity-unrelated
features in the target domain are distilled from the latent features. As a
result, the ReID features better encompass the identity of a person in the
unsupervised domain. We conducted experiments on the Market1501, DukeMTMC and
MSMT17 datasets. Results show that the unsupervised domain adaptation problem
in ReID is very challenging. Nevertheless, our method shows improvement in half
of the domain transfers and achieve state-of-the-art performance for one of
them.
