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.
Experiments on Online Test-Time Adaptation Scenario In addition to CTTA scenarios, our method is readily applicable to a wide range of TTA scenarios
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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.