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Test-time adaptation (TTA) methods, which generally rely on the model'spredictions (e.g., entropy minimization) to adapt the source pretrained modelto the unlabeled target domain, suffer from noisy signals originating from 1)incorrect or 2) open-set predictions. Long-term stable adaptation is hamperedby such noisy signals, so training models without such error accumulation iscrucial for practical TTA. To address these issues, including open-set TTA, wepropose a simple yet effective sample selection method inspired by thefollowing crucial empirical finding. While entropy minimization compels themodel to increase the probability of its predicted label (i.e., confidencevalues), we found that noisy samples rather show decreased confidence values.To be more specific, entropy minimization attempts to raise the confidencevalues of an individual sample's prediction, but individual confidence valuesmay rise or fall due to the influence of signals from numerous otherpredictions (i.e., wisdom of crowds). Due to this fact, noisy signalsmisaligned with such 'wisdom of crowds', generally found in the correctsignals, fail to raise the individual confidence values of wrong samples,despite attempts to increase them. Based on such findings, we filter out thesamples whose confidence values are lower in the adapted model than in theoriginal model, as they are likely to be noisy. Our method is widely applicableto existing TTA methods and improves their long-term adaptation performance inboth image classification (e.g., 49.4% reduced error rates with TENT) andsemantic segmentation (e.g., 11.7% gain in mIoU with TENT).

Towards Open-Set Test-Time Adaptation Utilizing the Wisdom of Crowds in Entropy Minimization