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Found 4 papers, 2 papers with code
Unsupervised Domain Adaptation Based on the Predictive Uncertainty of Models
Unsupervised domain adaptation (UDA) aims to improve the prediction performance in the target domain under distribution shifts from the source domain.
Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptation
Most unsupervised domain adaptation (UDA) methods assume that labeled source images are available during model adaptation.
Domain Generalization by Marginal Transfer Learning
In the problem of domain generalization (DG), there are labeled training data sets from several related prediction problems, and the goal is to make accurate predictions on future unlabeled data sets that are not known to the learner.
Generalizing from Several Related Classification Tasks to a New Unlabeled Sample
We develop a distribution-free, kernel-based approach to the problem.
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