TIDE improves single-source domain generalization by training models to attend to local concept regions and correcting mispredictions at test time using concept signatures.
Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models
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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction
TIDE improves single-source domain generalization by training models to attend to local concept regions and correcting mispredictions at test time using concept signatures.