  Although a significant progress has been witnessed in supervised person
re-identification (re-id), it remains challenging to generalize re-id models to
new domains due to the huge domain gaps. Recently, there has been a growing
interest in using unsupervised domain adaptation to address this scalability
issue. Existing methods typically conduct adaptation on the representation
space that contains both id-related and id-unrelated factors, thus inevitably
undermining the adaptation efficacy of id-related features. In this paper, we
seek to improve adaptation by purifying the representation space to be adapted.
To this end, we propose a joint learning framework that disentangles
id-related/unrelated features and enforces adaptation to work on the id-related
feature space exclusively. Our model involves a disentangling module that
encodes cross-domain images into a shared appearance space and two separate
structure spaces, and an adaptation module that performs adversarial alignment
and self-training on the shared appearance space. The two modules are
co-designed to be mutually beneficial. Extensive experiments demonstrate that
the proposed joint learning framework outperforms the state-of-the-art methods
by clear margins.
