  Unsupervised domain adaptation addresses the problem of transferring
knowledge from a well-labeled source domain to an unlabeled target domain where
the two domains have distinctive data distributions. Thus, the essence of
domain adaptation is to mitigate the distribution divergence between the two
domains. The state-of-the-art methods practice this very idea by either
conducting adversarial training or minimizing a metric which defines the
distribution gaps. In this paper, we propose a new domain adaptation method
named Adversarial Tight Match (ATM) which enjoys the benefits of both
adversarial training and metric learning. Specifically, at first, we propose a
novel distance loss, named Maximum Density Divergence (MDD), to quantify the
distribution divergence. MDD minimizes the inter-domain divergence ("match" in
ATM) and maximizes the intra-class density ("tight" in ATM). Then, to address
the equilibrium challenge issue in adversarial domain adaptation, we consider
leveraging the proposed MDD into adversarial domain adaptation framework. At
last, we tailor the proposed MDD as a practical learning loss and report our
ATM. Both empirical evaluation and theoretical analysis are reported to verify
the effectiveness of the proposed method. The experimental results on four
benchmarks, both classical and large-scale, show that our method is able to
achieve new state-of-the-art performance on most evaluations. Codes and
datasets used in this paper are available at {\it github.com/lijin118/ATM}.
