发表机构
IIT Kharagpur; IBM Research; Amazon; Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校; IBM 研究院; 亚马逊; 印度理工学院卡拉格普尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究推理时领域持续变化下的持续测试时自适应问题，提出无源可控教师自适应方法，动态设置动量值并估计类原型，无需源数据，实验表明该方法优于多种需源数据的现有自适应框架。
AI 中文摘要
在许多现实场景中，推理期间遇到领域的持续变化很常见。因此，利用师生框架的持续测试时自适应（CTTA）技术受到关注，使模型部署后仍能持续自适应。在此框架中，使用加权平均的均值教师从测试数据生成伪标签用于自训练。均值教师使用高动量值作为学生参数的指数移动平均值更新，即使遇到不同测试数据分布也保持固定。为解决模型漂移问题，我们提出一种新颖的可控教师自适应方法，根据输入数据质量动态设置合适的动量值。此外，我们从源预训练模型估计类原型以帮助对齐目标数据。重要的是，我们的方法在管道的任何阶段都无需访问源数据或其统计信息，实现真正无源。我们在基准数据集上进行了大量实验，证明我们的方法优于不同的现有自适应框架，其中许多需要访问源数据。
英文摘要
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.
