这篇论文解决了持续测试时适应的关键问题，无需源数据就能动态调整教师模型，效果超过那些需要源数据的方法，很实用。
持续测试时适应(CTTA)中常用的教师-学生框架采用固定高动量值更新教师模型，导致不同数据分布下模型漂移。该论文提出动态设置动量值的受控教师适应方法，并利用源预训练模型类原型对齐目标数据，无需任何源数据或统计信息。在多个基准数据集上的实验表明，该方法优于需要源数据的多种现有适配框架。
Source-Free Controlled Adaptation of Teachers for Continual Test-Time Adaptation
In many real-world scenarios, encountering continual shifts in domain during inference is very common. Consequently, continual test-time adaptation (CTTA) techniques leveraging a teacher-student framework have gained prominence, allowing models to adapt continuously even after deployment. In such a framework, a weight-averaged mean teacher is used to produce pseudo-labels from test data for self-training. The mean teacher gets updated as an exponential moving average of the student parameters using a high value of momentum that is kept fixed even if different distributions of test data are encountered. To combat the resulting drift of the model, we propose a novel controlled teacher adaptation methodology that dynamically sets a proper momentum value depending on the quality of the incoming data. Additionally, we estimate class prototypes from the source pretrained model to help align the target data as they come in. Importantly, our method does not require access to source data or its statistics at any stage of the pipeline, making it truly source-free. We perform extensive experiments on benchmark datasets to demonstrate that our approach outperforms different state-of-the-art adaptation frameworks, many of which require access to source data.