Cross-domain Contrastive Learning for Unsupervised Domain Adaptation
This work addresses the problem of transferring knowledge between labeled source and unlabeled target domains for image classification, representing an incremental improvement over existing methods.
The paper tackles unsupervised domain adaptation by proposing a contrastive learning framework (CDCL) that aligns features across domains using cross-domain samples and pseudo labels, achieving state-of-the-art performance on Office-31 and VisDA-2017 benchmarks.
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.