SERL boosts source-free semi-supervised domain adaptation by combining probabilistic contrastive loss, easy-hard sample mixup, and early prediction regularization, reporting state-of-the-art accuracy on DomainNet, Office-Home, and Office-31.
Context-guided entropy minimization for semi-supervised domain adaptation,
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Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation
SERL boosts source-free semi-supervised domain adaptation by combining probabilistic contrastive loss, easy-hard sample mixup, and early prediction regularization, reporting state-of-the-art accuracy on DomainNet, Office-Home, and Office-31.