这篇论文提出了一个简单的改进方法，让教师模型不更新权重，就能显著提升测试时适应的性能。
研究人员挑战了测试时适应(TTA)中常用的教师-学生框架，发现指数移动平均方法仍会导致误差累积。论文提出使用不更新权值的固执教师(intransigent teacher)，显著提升了在长序列数据集上的性能。该方法增强了模型对超参数变化的鲁棒性，可无缝应用于多种架构和实验设置，包括语义分割。
Rethinking the Teacher-Student Framework for Test-Time Adaptation
Test-Time Adaptation (TTA) has recently emerged as a promising strategy that allows the adaptation of pre-trained models to changing data distributions at deployment time, without access to any labels. To mitigate error accumulation, researchers have widely adopted the teacher-student framework, though its long-term stability is often taken for granted. In this work, we challenge the common strategy of setting the teacher weights to an exponential moving average of the student by showing that error accumulation still occurs, although it is mostly apparent on longer sequences compared to those commonly utilized. We analyze the stability-plasticity trade-off within the teacher-student framework and propose to use an intransigent teacher that does not update its weights. Surprisingly, we show that this simple change allows TTA methods to significantly improve their performance on multiple datasets with longer scenarios and result in increased robustness to changes in hyperparameters. Finally, we show that those changes can be seamlessly and effectively applied to various architectures and experimental setups, including semantic segmentation. The code is available at https://github.com/dmn-sjk/intransigent_teacher.