Link-based Contrastive Learning for One-Shot Unsupervised Domain Adaptation

Abstract

Unsupervised domain adaptation (UDA) transfers knowledge from a labeled source domain to an unlabeled target domain via distribution alignment. However, in real-world scenarios like public safety or access control, obtaining sufficient source data is challenging, limiting existing UDA methods. This paper investigates a realistic but rarely studied problem called one-shot unsupervised domain adaptation (OSUDA), where only one source example per category is available. OSUDA faces dual challenges in feature learning and domain alignment due to the extreme source data scarcity. To address these, we propose link-based contrastive learning (LCL), a simple yet effective approach for OSUDA. LCL leverages in-domain links to learn discriminative features from abundant unlabeled target data and cross-domain links to achieve precise domain alignment with only one source sample per category. Extensive experiments on four domain adaptation benchmarks (VisDA-2017, Office-31, Office-Home, and DomainNet) demonstrate LCL’s effectiveness under the OSUDA setting. Additionally, we construct a real-world OSUDA surveillance face recognition dataset, where LCL consistently improves recognition performance across various face recognition methods.