Domain generalization (DG) is a prevalent problem in real-world applicat...
Universal domain adaptation (UniDA) aims to transfer knowledge from the
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Prompt learning has become one of the most efficient paradigms for adapt...
Masked image modeling (MIM) learns visual representation by masking and
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Domain Generalization (DG) aims to learn a model that can generalize wel...
Considerable progress has been made in domain generalization (DG) which ...
Unsupervised domain adaptation (UDA) aims to learn transferable knowledg...
Domain generalization (DG) aims to learn a generalizable model from mult...
Domain generalization (DG) aims to learn from multiple source domains a ...
Domain generalization (DG) utilizes multiple labeled source datasets to ...
Instrumental variables (IVs), sources of treatment randomization that ar...
One fundamental problem in the learning treatment effect from observatio...
In this paper, the concept of subgraph network (SGN) is introduced and t...