  Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from
a fully-labeled source domain to a different unlabeled target domain. Most
existing UDA methods learn domain-invariant feature representations by
minimizing feature distances across domains. In this work, we build upon
contrastive self-supervised learning to align features so as to reduce the
domain discrepancy between training and testing sets. Exploring the same set of
categories shared by both domains, we introduce a simple yet effective
framework CDCL, for domain alignment. In particular, given an anchor image from
one domain, we minimize its distances to cross-domain samples from the same
class relative to those from different categories. Since target labels are
unavailable, we use a clustering-based approach with carefully initialized
centers to produce pseudo labels. In addition, we demonstrate that CDCL is a
general framework and can be adapted to the data-free setting, where the source
data are unavailable during training, with minimal modification. We conduct
experiments on two widely used domain adaptation benchmarks, i.e., Office-31
and VisDA-2017, for image classification tasks, and demonstrate that CDCL
achieves state-of-the-art performance on both datasets.
