  Although unsupervised domain adaptation methods have been widely adopted
across several computer vision tasks, it is more desirable if we can exploit a
few labeled data from new domains encountered in a real application. The novel
setting of the semi-supervised domain adaptation (SSDA) problem shares the
challenges with the domain adaptation problem and the semi-supervised learning
problem. However, a recent study shows that conventional domain adaptation and
semi-supervised learning methods often result in less effective or negative
transfer in the SSDA problem. In order to interpret the observation and address
the SSDA problem, in this paper, we raise the intra-domain discrepancy issue
within the target domain, which has never been discussed so far. Then, we
demonstrate that addressing the intra-domain discrepancy leads to the ultimate
goal of the SSDA problem. We propose an SSDA framework that aims to align
features via alleviation of the intra-domain discrepancy. Our framework mainly
consists of three schemes, i.e., attraction, perturbation, and exploration.
First, the attraction scheme globally minimizes the intra-domain discrepancy
within the target domain. Second, we demonstrate the incompatibility of the
conventional adversarial perturbation methods with SSDA. Then, we present a
domain adaptive adversarial perturbation scheme, which perturbs the given
target samples in a way that reduces the intra-domain discrepancy. Finally, the
exploration scheme locally aligns features in a class-wise manner complementary
to the attraction scheme by selectively aligning unlabeled target features
complementary to the perturbation scheme. We conduct extensive experiments on
domain adaptation benchmark datasets such as DomainNet, Office-Home, and
Office. Our method achieves state-of-the-art performances on all datasets.
