  This article studies the domain adaptation problem in person
re-identification (re-ID) under a "learning via translation" framework,
consisting of two components, 1) translating the labeled images from the source
to the target domain in an unsupervised manner, 2) learning a re-ID model using
the translated images. The objective is to preserve the underlying human
identity information after image translation, so that translated images with
labels are effective for feature learning on the target domain. To this end, we
propose a similarity preserving generative adversarial network (SPGAN) and its
end-to-end trainable version, eSPGAN. Both aiming at similarity preserving,
SPGAN enforces this property by heuristic constraints, while eSPGAN does so by
optimally facilitating the re-ID model learning. More specifically, SPGAN
separately undertakes the two components in the "learning via translation"
framework. It first preserves two types of unsupervised similarity, namely,
self-similarity of an image before and after translation, and
domain-dissimilarity of a translated source image and a target image. It then
learns a re-ID model using existing networks. In comparison, eSPGAN seamlessly
integrates image translation and re-ID model learning. During the end-to-end
training of eSPGAN, re-ID learning guides image translation to preserve the
underlying identity information of an image. Meanwhile, image translation
improves re-ID learning by providing identity-preserving training samples of
the target domain style. In the experiment, we show that identities of the fake
images generated by SPGAN and eSPGAN are well preserved. Based on this, we
report the new state-of-the-art domain adaptation results on two large-scale
person re-ID datasets.
