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 neural-network approximation errors. Under the stated assumptions and common H"older smoothness β≥1, the bound identifies a sufficiently large unlabeled-sample regime in which the downstream rate is widetildeO(m-β/(2β+d)), where m counts labeled source samples and logarithmic factors are suppressed. The labeled-sample exponent is governed by the latent dimension d rather than the ambient dimension p, which enters the pretraining cost. Experiments on Office-31, Office-Home, and DomainNet demonstrate clear transfer gains across diverse domain shifts, with particularly strong performance when representations are learned from scratch.