  We address the problem of person re-identification (reID), that is,
retrieving person images from a large dataset, given a query image of the
person of interest. A key challenge is to learn person representations robust
to intra-class variations, as different persons can have the same attribute and
the same person's appearance looks different with viewpoint changes. Recent
reID methods focus on learning discriminative features but robust to only a
particular factor of variations (e.g., human pose), which requires
corresponding supervisory signals (e.g., pose annotations). To tackle this
problem, we propose to disentangle identity-related and -unrelated features
from person images. Identity-related features contain information useful for
specifying a particular person (e.g., clothing), while identity-unrelated ones
hold other factors (e.g., human pose, scale changes). To this end, we introduce
a new generative adversarial network, dubbed \emph{identity shuffle GAN}
(IS-GAN), that factorizes these features using identification labels without
any auxiliary information. We also propose an identity-shuffling technique to
regularize the disentangled features. Experimental results demonstrate the
effectiveness of IS-GAN, significantly outperforming the state of the art on
standard reID benchmarks including the Market-1501, CUHK03 and DukeMTMC-reID.
Our code and models are available online:
https://cvlab-yonsei.github.io/projects/ISGAN/.
