  Test-time adaptation (TTA) is the problem of updating a pre-trained source
model at inference time given test input(s) from a different target domain.
Most existing TTA approaches assume the setting in which the target domain is
stationary, i.e., all the test inputs come from a single target domain.
However, in many practical settings, the test input distribution might exhibit
a lifelong/continual shift over time. Moreover, existing TTA approaches also
lack the ability to provide reliable uncertainty estimates, which is crucial
when distribution shifts occur between the source and target domain. To address
these issues, we present PETAL (Probabilistic lifElong Test-time Adaptation
with seLf-training prior), which solves lifelong TTA using a probabilistic
approach, and naturally results in (1) a student-teacher framework, where the
teacher model is an exponential moving average of the student model, and (2)
regularizing the model updates at inference time using the source model as a
regularizer. To prevent model drift in the lifelong/continual TTA setting, we
also propose a data-driven parameter restoration technique which contributes to
reducing the error accumulation and maintaining the knowledge of recent domains
by restoring only the irrelevant parameters. In terms of predictive error rate
as well as uncertainty based metrics such as Brier score and negative
log-likelihood, our method achieves better results than the current
state-of-the-art for online lifelong test-time adaptation across various
benchmarks, such as CIFAR-10C, CIFAR-100C, ImageNetC, and ImageNet3DCC
datasets. The source code for our approach is accessible at
https://github.com/dhanajitb/petal.
