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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 rep
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