  In this work, we study Unsupervised Domain Adaptation (UDA) in a challenging
self-supervised approach. One of the difficulties is how to learn task
discrimination in the absence of target labels. Unlike previous literature
which directly aligns cross-domain distributions or leverages reverse gradient,
we propose Domain Confused Contrastive Learning (DCCL) to bridge the source and
the target domains via domain puzzles, and retain discriminative
representations after adaptation. Technically, DCCL searches for a most
domain-challenging direction and exquisitely crafts domain confused
augmentations as positive pairs, then it contrastively encourages the model to
pull representations towards the other domain, thus learning more stable and
effective domain invariances. We also investigate whether contrastive learning
necessarily helps with UDA when performing other data augmentations. Extensive
experiments demonstrate that DCCL significantly outperforms baselines.
