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Found 2 papers, 1 papers with code
Global-Local Regularization Via Distributional Robustness
First, they purely focus on local regularization to strengthen model robustness, missing a global regularization effect which is useful in many real-world applications (e. g., domain adaptation, domain generalization, and adversarial machine learning).
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources
Domain adaptation (DA) benefits from the rigorous theoretical works that study its insightful characteristics and various aspects, e. g., learning domain-invariant representations and its trade-off.
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