Joint Contrastive-Adversarial Representation Learning for Unsupervised Domain Adaptation
Effective unsupervised domain adaptation (UDA) requires a representation that captures transferable structure from both the labeled source domain and the unlabeled target domain. We introduce Joint Contrastive-Adversarial Representation Learning (JCAR), a two-stage framework that learns a common representation from the union of unlabeled source and target samples by combining contrastive learning with adversarial domain alignment, and then freezes the representation to fit a source-supervised predictor. We establish a non-asymptotic excess-risk bound for this procedure that couples representation and domain-discriminator estimation while separating downstream prediction, with explicit neu...
| Authors | Yuling Jiao, Lican Kang, Jerry Zhijian Yang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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