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In this paper, we propose energy-based sample adaptation at test time for domain generalization.
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Test-time classifier adjustment module for model-agnostic domain generalization
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Ttt++: When does self-supervised test-time training fail or thrive?
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On learning domain-invariant representations for transfer learning with multiple sources
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How to train your energy-based models
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Tent: Fully test-time adaptation by entropy minimization
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Adaptive risk minimization: Learning to adapt to domain shift
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Training on test data with bayesian adaptation for covariate shift
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Domain generalization: A survey
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Unsupervised energy-based adversarial domain adaptation for cross-domain text classification
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Association graph learning for multi-task classification with category shifts
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A tale of two flows: cooperative learning of langevin flow and normalizing flow toward energy-based model
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Improving out-of-distribution robustness via selective augmentation
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