Traditional test-time adaptation (TTA) methods face significant challenges in
adapting to dynamic environments characterized by continuously changing
long-term target distributions. These challenges primarily stem from two
factors: catastrophic forgetting of previously learned valuable source
knowledge and gradual error accumulation caused by miscalibrated pseudo labels.
To address these issues, this paper introduces an unsupervised domain change
detection method that is capable of identifying domain shifts in dynamic
environments and subsequently resets the model parameters to the original
source pre-trained values. By restoring the knowledge from the source, it
effectively corrects the negative consequences arising from the gradual
deterioration of model parameters caused by ongoing shifts in the domain. Our
method involves progressive estimation of global batch-norm statistics specific
to each domain, while keeping track of changes in the statistics triggered by
domain shifts. Importantly, our method is agnostic to the specific adaptation
technique employed and thus, can be incorporated to existing TTA methods to
enhance their performance in dynamic environments. We perform extensive
experiments on benchmark datasets to demonstrate the superior performance of
our method compared to state-of-the-art adaptation methods.

通过无监督领域变化检测方法，该论文解决了传统测试时适应方法在动态环境中适应连续变化的目标分布时所面临的挑战，包括之前学习的有价值源知识的灾难性遗忘和由于错误标签的错误校准而导致的渐进性误差积累问题。