  Since distribution shifts are likely to occur after a model's deployment and
can drastically decrease the model's performance, online test-time adaptation
(TTA) continues to update the model during test-time, leveraging the current
test data. In real-world scenarios, test data streams are not always
independent and identically distributed (i.i.d.). Instead, they are frequently
temporally correlated, making them non-i.i.d. Many existing methods struggle to
cope with this scenario. In response, we propose a diversity-aware and
category-balanced buffer that can simulate an i.i.d. data stream, even in
non-i.i.d. scenarios. Combined with a diversity and entropy-weighted entropy
loss, we show that a stable adaptation is possible on a wide range of
corruptions and natural domain shifts, based on ImageNet. We achieve
state-of-the-art results on most considered benchmarks.

Computer Vision and Pattern Recognition