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Since experiencing domain shifts during test-time is inevitable in practice,test-time adaption (TTA) continues to adapt the model after deployment.Recently, the area of continual and gradual test-time adaptation (TTA) emerged.In contrast to standard TTA, continual TTA considers not only a single domainshift, but a sequence of shifts. Gradual TTA further exploits the property thatsome shifts evolve gradually over time. Since in both settings long testsequences are present, error accumulation needs to be addressed for methodsrelying on self-training. In this work, we propose and show that in the settingof TTA, the symmetric cross-entropy is better suited as a consistency loss formean teachers compared to the commonly used cross-entropy. This is justified byour analysis with respect to the (symmetric) cross-entropy's gradientproperties. To pull the test feature space closer to the source domain, wherethe pre-trained model is well posed, contrastive learning is leveraged. Sinceapplications differ in their requirements, we address several settings,including having source data available and the more challenging source-freesetting. We demonstrate the effectiveness of our proposed method 'robust meanteacher' (RMT) on the continual and gradual corruption benchmarks CIFAR10C,CIFAR100C, and Imagenet-C. We further consider ImageNet-R and propose a newcontinual DomainNet-126 benchmark. State-of-the-art results are achieved on allbenchmarks.

Robust Mean Teacher for Continual and Gradual Test-Time Adaptation