  Recently, contrastive self-supervised learning has become a key component for
learning visual representations across many computer vision tasks and
benchmarks. However, contrastive learning in the context of domain adaptation
remains largely underexplored. In this paper, we propose to extend contrastive
learning to a new domain adaptation setting, a particular situation occurring
where the similarity is learned and deployed on samples following different
probability distributions without access to labels. Contrastive learning learns
by comparing and contrasting positive and negative pairs of samples in an
unsupervised setting without access to source and target labels. We have
developed a variation of a recently proposed contrastive learning framework
that helps tackle the domain adaptation problem, further identifying and
removing possible negatives similar to the anchor to mitigate the effects of
false negatives. Extensive experiments demonstrate that the proposed method
adapts well, and improves the performance on the downstream domain adaptation
task.
