SEVA replaces the entropy loss used in test-time adaptation with a closed-form upper bound that mimics infinitely many vicinal augmentations in one backward pass, and uses this bound to filter unreliable samples.
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SEVA: Leveraging Single-Step Ensemble of Vicinal Augmentations for Test-Time Adaptation
SEVA replaces the entropy loss used in test-time adaptation with a closed-form upper bound that mimics infinitely many vicinal augmentations in one backward pass, and uses this bound to filter unreliable samples.