  Experiencing domain shifts during test-time is nearly inevitable in practice
and likely results in a severe performance degradation. To overcome this issue,
test-time adaptation continues to update the initial source model during
deployment. A promising direction are methods based on self-training which have
been shown to be well suited for gradual domain adaptation, since reliable
pseudo-labels can be provided. In this work, we address two problems that exist
when applying self-training in the setting of test-time adaptation. First,
adapting a model to long test sequences that contain multiple domains can lead
to error accumulation. Second, naturally, not all shifts are gradual in
practice. To tackle these challenges, we introduce GTTA. By creating artificial
intermediate domains that divide the current domain shift into a more gradual
one, effective self-training through high quality pseudo-labels can be
performed. To create the intermediate domains, we propose two independent
variations: mixup and light-weight style transfer. We demonstrate the
effectiveness of our approach on the continual and gradual corruption
benchmarks, as well as ImageNet-R. To further investigate gradual shifts in the
context of urban scene segmentation, we publish a new benchmark: CarlaTTA. It
enables the exploration of several non-stationary domain shifts.
