  We study the problem of unsupervised domain adaptive re-identification
(re-ID) which is an active topic in computer vision but lacks a theoretical
foundation. We first extend existing unsupervised domain adaptive
classification theories to re-ID tasks. Concretely, we introduce some
assumptions on the extracted feature space and then derive several loss
functions guided by these assumptions. To optimize them, a novel self-training
scheme for unsupervised domain adaptive re-ID tasks is proposed. It iteratively
makes guesses for unlabeled target data based on an encoder and trains the
encoder based on the guessed labels. Extensive experiments on unsupervised
domain adaptive person re-ID and vehicle re-ID tasks with comparisons to the
state-of-the-arts confirm the effectiveness of the proposed theories and
self-training framework. Our code is available at
\url{https://github.com/LcDog/DomainAdaptiveReID}.
