  Unsupervised domain adaptation uses source data from different distributions
to solve the problem of classifying data from unlabeled target domains.
However, conventional methods require access to source data, which often raise
concerns about data privacy. In this paper, we consider a more practical but
challenging setting where the source domain data is unavailable and the target
domain data is unlabeled. Specifically, we address the domain discrepancy
problem from the perspective of contrastive learning. The key idea of our work
is to learn a domain-invariant feature by 1) performing clustering directly in
the original feature space with nearest neighbors; 2) constructing truly hard
negative pairs by extended neighbors without introducing additional
computational complexity; and 3) combining noise-contrastive estimation theory
to gain computational advantage. We conduct careful ablation studies and
extensive experiments on three common benchmarks: VisDA, Office-Home, and
Office-31. The results demonstrate the superiority of our methods compared with
other state-of-the-art works.
