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Since distribution shifts are likely to occur during test-time and candrastically decrease the model's performance, online test-time adaptation (TTA)continues to update the model after deployment, leveraging the current testdata. Clearly, a method proposed for online TTA has to perform well for allkinds of environmental conditions. By introducing the variable factors domainnon-stationarity and temporal correlation, we first unfold all practicallyrelevant settings and define the entity as universal TTA. We want to highlightthat this is the first work that covers such a broad spectrum, which isindispensable for the use in practice. To tackle the problem of universal TTA,we identify and highlight several challenges a self-training based method hasto deal with: 1) model bias and the occurrence of trivial solutions whenperforming entropy minimization on varying sequence lengths with and withoutmultiple domain shifts, 2) loss of generalization which exacerbates theadaptation to multiple domain shifts and the occurrence of catastrophicforgetting, and 3) performance degradation due to shifts in class prior. Toprevent the model from becoming biased, we leverage a dataset andmodel-agnostic certainty and diversity weighting. In order to maintaingeneralization and prevent catastrophic forgetting, we propose to continuallyweight-average the source and adapted model. To compensate for disparities inthe class prior during test-time, we propose an adaptive prior correctionscheme that reweights the model's predictions. We evaluate our approach, namedROID, on a wide range of settings, datasets, and models, setting new standardsin the field of universal TTA. Code is available at:https://github.com/mariodoebler/test-time-adaptation

Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correction