Person re-identification (re-ID) models trained on one domain often fail to
generalize well to another. In our attempt, we present a "learning via
translation" framework...In the baseline, we translate the labeled images from
source to target domain in an unsupervised manner. We then train re-ID models
with the translated images by supervised methods. Yet, being an essential part
of this framework, unsupervised image-image translation suffers from the
information loss of source-domain labels during translation. Our motivation is two-fold. First, for each image, the discriminative cues
contained in its ID label should be maintained after translation. Second, given
the fact that two domains have entirely different persons, a translated image
should be dissimilar to any of the target IDs. To this end, we propose to
preserve two types of unsupervised similarities, 1) self-similarity of an image
before and after translation, and 2) domain-dissimilarity of a translated
source image and a target image. Both constraints are implemented in the
similarity preserving generative adversarial network (SPGAN) which consists of
an Siamese network and a CycleGAN. Through domain adaptation experiment, we
show that images generated by SPGAN are more suitable for domain adaptation and
yield consistent and competitive re-ID accuracy on two large-scale datasets.(read more)