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Found 2 papers, 1 papers with code
Domain Generalization via Semi-supervised Meta Learning
To learn a domain-invariant representation, it also utilizes a novel alignment loss to ensure that the distance between pairs of class centroids, computed after adding the unlabeled samples, is preserved across different domains.
Adversarial Inductive Transfer Learning with input and output space adaptation
To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies.
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