  This work considers the problem of unsupervised domain adaptation in person
re-identification (re-ID), which aims to transfer knowledge from the source
domain to the target domain. Existing methods are primary to reduce the
inter-domain shift between the domains, which however usually overlook the
relations among target samples. This paper investigates into the intra-domain
variations of the target domain and proposes a novel adaptation framework
w.r.t. three types of underlying invariance, i.e., Exemplar-Invariance,
Camera-Invariance, and Neighborhood-Invariance. Specifically, an exemplar
memory is introduced to store features of samples, which can effectively and
efficiently enforce the invariance constraints over the global dataset. We
further present the Graph-based Positive Prediction (GPP) method to explore
reliable neighbors for the target domain, which is built upon the memory and is
trained on the source samples. Experiments demonstrate that 1) the three
invariance properties are indispensable for effective domain adaptation, 2) the
memory plays a key role in implementing invariance learning and improves the
performance with limited extra computation cost, 3) GPP could facilitate the
invariance learning and thus significantly improves the results, and 4) our
approach produces new state-of-the-art adaptation accuracy on three re-ID
large-scale benchmarks.
