By ranking predictions across progressively masked views, REM reduces model collapse in entropy-based test-time adaptation and improves CTTA accuracy on ImageNet-C, CIFAR10-C, and CIFAR100-C.
Following W ATT, we report the TTA results from the CLIP model to CIFAR (Krizhevsky et al.,
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Ranked Entropy Minimization for Continual Test-Time Adaptation
By ranking predictions across progressively masked views, REM reduces model collapse in entropy-based test-time adaptation and improves CTTA accuracy on ImageNet-C, CIFAR10-C, and CIFAR100-C.