  Since distribution shifts are likely to occur during test-time and can
drastically decrease the model's performance, online test-time adaptation (TTA)
continues to update the model after deployment, leveraging the current test
data. Clearly, a method proposed for online TTA has to perform well for all
kinds of environmental conditions. By introducing the variable factors domain
non-stationarity and temporal correlation, we first unfold all practically
relevant settings and define the entity as universal TTA. We want to highlight
that this is the first work that covers such a broad spectrum, which is
indispensable for the use in practice. To tackle the problem of universal TTA,
we identify and highlight several challenges a self-training based method has
to deal with: 1) model bias and the occurrence of trivial solutions when
performing entropy minimization on varying sequence lengths with and without
multiple domain shifts, 2) loss of generalization which exacerbates the
adaptation to multiple domain shifts and the occurrence of catastrophic
forgetting, and 3) performance degradation due to shifts in class prior. To
prevent the model from becoming biased, we leverage a dataset and
model-agnostic certainty and diversity weighting. In order to maintain
generalization and prevent catastrophic forgetting, we propose to continually
weight-average the source and adapted model. To compensate for disparities in
the class prior during test-time, we propose an adaptive prior correction
scheme that reweights the model's predictions. We evaluate our approach, named
ROID, on a wide range of settings, datasets, and models, setting new standards
in the field of universal TTA. Code is available at:
https://github.com/mariodoebler/test-time-adaptation
