College of Electric and Information, Northeast Agricultural University（东北农业大学电气与信息学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
持续测试时适应（CTTA）旨在在连续领域偏移下，于测试阶段使预训练模型适应不断变化的环境。大多数现有CTTA方法基于Mean Teacher（MT）结构，该结构包含学生模型和教师模型：学生模型利用教师模型生成的伪标签进行更新，教师模型随后通过指数移动平均策略更新。然而，这些方法不加区分地更新MT模型的所有参数；也就是说，一些涉及跨不同领域共享知识的关键参数可能被擦除，从而加剧错误累积和灾难性遗忘。本文提出Parameter-Selective Mean Teacher（PSMT，参数选择性平均教师）方法，能够在领域偏移下有效更新MT网络中的关键参数。首先，我们在学生模型中引入选择性蒸馏机制，利用过去知识正则化新知识，从而减轻错误累积的影响。其次，为避免灾难性遗忘，在教师模型中，我们通过Fisher信息创建掩码，利用指数移动平均选择性地更新参数，并对关键参数采取保护措施。大量实验结果证明，PSMT在多个基准数据集上优于最先进方法。代码可在https://github.com/JiaxuTian/PSMT获取。
英文摘要：
Continual Test-Time Adaptation (CTTA) aims to adapt a pretrained model to ever-changing environments during the test time under continuous domain shifts. Most existing CTTA approaches are based on the Mean Teacher (MT) structure, which contains a student and a teacher model, where the student is updated using the pseudo-labels from the teacher model, and the teacher is then updated by exponential moving average strategy. However, these methods update the MT model indiscriminately on all parameters of the model. That is, some critical parameters involving sharing knowledge across different domains may be erased, intensifying error accumulation and catastrophic forgetting. In this paper, we introduce Parameter-Selective Mean Teacher (PSMT) method, which is capable of effectively updating the critical parameters within the MT network under domain shifts. First, we introduce a selective distillation mechanism in the student model, which utilizes past knowledge to regularize novel knowledge, thereby mitigating the impact of error accumulation. Second, to avoid catastrophic forgetting, in the teacher model, we create a mask through Fisher information to selectively update parameters via exponential moving average, with preservation measures applied to crucial parameters. Extensive experimental results verify that PSMT outperforms state-of-the-art methods across multiple benchmark datasets. Our code is available at \url{https://github.com/JiaxuTian/PSMT}.
The Golden Subspace: Where Efficiency Meets Generalization in Continual Test-Time Adaptation
School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；Hong Kong University of Science and Technology（香港科技大学）；Frontier Robotics（前沿机器人）
FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning
1 Center for Computational \& Data Sciences, Independent University, Bangladesh；2 Curtin University；Center for Computational \& Data Sciences Independent University, Bangladesh；School of Electrical Engineering, Computing \& Mathematical Sciences (EECMS) Curtin University
Subspace Optimization for Backpropagation-Free Continual Test-Time Adaptation
Poznan University of Technology（波兹南理工大学）；IDEAS NCBR；NASK - National Research Institute（NASK国家研究所）；Gdańsk University of Technology（格但斯克理工大学）；Graz University of Technology（格拉茨理工大学）
Divide and not forget: Ensemble of selectively trained experts in Continual Learning
IDEAS-NCBR；Warsaw University of Technology（华沙工业大学）；Gdańsk University of Technology（格但斯克工业大学）；Tooploox；Jagiellonian University（雅盖隆大学）；Computer Vision Center, Barcelona（巴塞罗那计算机视觉中心）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
ElementAI（ElementAI公司）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Facebook AI Research（脸书人工智能研究院）；McGill University（麦吉尔大学）；HEC Montréal（蒙特利尔高等商学院）
Conformal Uncertainty Indicator for Continual Test-Time Adaptation
New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所新模式识别实验室）；China Nuclear Power Engineering Co., LTD（中国核电工程有限公司）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；School of Computer Science & Engineering, LinYi University（临沂大学计算机科学与工程学院）；School of Electric & Information Engineering, Suzhou University of Science and Technology（苏州科技大学电子与信息工程学院）
C-LoRA: Continual Low-Rank Adaptation for Pre-trained Visual Models
Institute of Intelligent Information Processing, Shanxi University（山西大学智能信息处理研究所）；Alliance Manchester Business School, The University of Manchester（曼彻斯特大学联盟曼彻斯特商学院）
SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Chongqing University（重庆大学）；Peng Cheng Laboratory（鹏城实验室）；Wuhan AI Research（武汉人工智能研究院）
EDAPT: Towards Calibration-Free BCIs with Continual Online Adaptation
Excellence Cluster Machine Learning, University of Tübingen（卓越集群机器学习中心，图宾根大学）；Tübingen AI Center, University of Tübingen（图宾根人工智能中心，图宾根大学）；Department of Neurology and Stroke, University of Tübingen（神经学与中风部门，图宾根大学）；Hertie Institute for Clinical Brain Research, University of Tübingen（赫尔特研究所，图宾根大学）；Department of Empirical Inference, Max Planck Institute for Intelligent Systems（经验推理部门，智能系统马克斯·普朗克研究所）；Hertie Institute for AI in Brain Health, Tübingen（赫尔特人工智能与脑健康研究所，图宾根）
