Continual Test-Time Adaptation (CTA) is a challenging task that aims to adapt
a source pre-trained model to continually changing target domains. In the CTA
setting, a model does not know when the target domain changes, thus facing a
drastic change in the distribution of streaming inputs during the test-time.
The key challenge is to keep adapting the model to the continually changing
target domains in an online manner. We find that a model shows highly biased
predictions as it constantly adapts to the chaining distribution of the target
data. It predicts certain classes more often than other classes, making
inaccurate over-confident predictions. This paper mitigates this issue to
improve performance in the CTA scenario. To alleviate the bias issue, we make
class-wise exponential moving average target prototypes with reliable target
samples and exploit them to cluster the target features class-wisely. Moreover,
we aim to align the target distributions to the source distribution by
anchoring the target feature to its corresponding source prototype. With
extensive experiments, our proposed method achieves noteworthy performance gain
when applied on top of existing CTA methods without substantial adaptation time
overhead.

本文研究了在不断变化的目标领域中将源预训练模型适应的困难任务，发现源模型对目标数据流分布的不断改变表现出高度偏向的预测，本文提出了一种解决偏见问题的方法，并在现有的连续测试时间适应方法基础上取得了显著的性能提升，而适应时间开销并不大。