TIDE improves single-source domain generalization by training models to attend to local concept regions and correcting mispredictions at test time using concept signatures.
A-star: Test-time attention segregation and retention for text-to-image synthesis
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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.