Contrastive Domain Adaptation

Understand

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.