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arXiv 2511.20696cs.LGcs.AI
基于原型的非示例持续学习用于跨受体EEG解码
Prototype-Guided Non-Exemplar Continual Learning for Cross-subject EEG Decoding
Dept. of Artificial Intelligence Korea University Seoul, Republic of Korea（人工智能系 韩国大学首尔共和国）；Cognitive Engineering Korea University Seoul, Republic of Korea（认知工程 韩国大学首尔共和国）
Dan Li, Hye-Bin Shin, Yeon-Woo Choi
备注：
4 pages, 2 figures, 14th IEEE International Winter Conference on Brain-Computer Interface Conference 2026
Due to the significant variability in electroencephalo-gram (EEG) signals across individuals, knowledge acquired from previous subjects is often overwritten as new subjects are introduced in continual EEG decoding tasks. Existing methods mainly rely on storing historical data from seen subjects as replay buffers to mitigate forgetting, which is impractical under privacy or memory constraints. To address this issue, we propose a Prototype-guided Non-Exemplar Continual Learning (ProNECL) framework that preserves prior knowledge without accessing historical EEG samples. ProNECL summarizes subject-specific discriminative representations into class-level prototypes and incrementally aligns new subject representations with a global prototype memory through prototype-based feature regulariza-tion and cross-subject alignment. Experiments on the BCI Com-petition IV 2a and 2b datasets demonstrate that ProNECL effec-tively balances knowledge retention and adaptability, achieving superior performance in cross-subject continual EEG decoding tasks.
SPICED: A Synaptic Homeostasis-Inspired Framework for Unsupervised Continual EEG Decoding
State Key Laboratory of Brain-machine Intelligence（脑机智能国家重点实验室）；College of Computer Science and Technology（计算机科学与技术学院）；Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital（神经生物学系、附属心理健康中心及杭州市第七人民医院）；MOE Frontier Science Center for Brain Science and Brain-machine Integration（教育部脑科学与脑机整合前沿科学中心）
Memory-efficient Continual Learning with Prototypical Exemplar Condensation
College of Engineering and Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；Center for Environmental Intelligence, VinUniversity（VinUniversity环境智能中心）；Center for AI Research, VinUniversity（VinUniversity人工智能研究中心）；Faculty of Information Systems, Phenikaa University（Phenikaa大学信息系统学院）
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
Warsaw University of Technology（华沙理工大学）；IDEAS NCBR；Jagiellonian University（雅盖隆大学）；Gdańsk University of Technology（格但斯克理工大学）；Autonomous University of Barcelona（巴塞罗那自治大学）；Computer Vision Center（计算机视觉中心）；Tooploox
Swiss Federal Institute of Technology Lausanne (EPFL)（洛桑联邦理工学院）
Session-based recommendation has received growing attention recently due to the increasing privacy concern. Despite the recent success of neural session-based recommenders, they are typically developed in an offline manner using a static dataset. However, recommendation requires continual adaptation to take into account new and obsolete items and users, and requires "continual learning" in real-life applications. In this case, the recommender is updated continually and periodically with new data that arrives in each update cycle, and the updated model needs to provide recommendations for user …
Ca’ Foscari University（威尼斯福斯卡里大学）；University of New South Wales（新南威尔士大学）；Computer Vision Center（计算机视觉中心）
In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their abi…
LoRA Subtraction for Drift-Resistant Space in Exemplar-Free Continual Learning
School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；Key Laboratory of Intelligent Assessment Technology for Sustainable Tourism, Ministry of Culture and Tourism, Sun Yat-sen University（文化和旅游部可持续旅游智能评估技术重点实验室）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China（教育部机器智能与先进计算重点实验室）
South China University of Technology（华南理工大学）；Southern University of Science and Technology（南方科技大学）；Shenzhen Institute, Hunan University（湖南大学深圳研究院）；Pazhou Lab（琶洲实验室）
ICICLE: Interpretable Class Incremental Continual Learning
Jagiellonian University（雅盖隆大学）；Ardigen SA（阿迪根公司）；Autonomous University of Barcelona（巴塞罗那自治大学）；Computer Vision Center（计算机视觉中心）；IDEAS NCBR（IDEAS国家计算智能研究中心）
FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning
Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Computer Vision Center, Barcelona University of Chinese Academy of Sciences（巴塞罗那大学计算机视觉中心，中国科学院）；State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所机器人学国家重点实验室）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造研究所）
Natural language generation (NLG) is an essential component of task-oriented dialog systems. Despite the recent success of neural approaches for NLG, they are typically developed in an offline manner for particular domains. To better fit real-life applications where new data come in a stream, we study NLG in a "continual learning" setting to expand its knowledge to new domains or functionalities incrementally. The major challenge towards this goal is catastrophic forgetting, meaning that a continually trained model tends to forget the knowledge it has learned before. To this end, we propose a …
Continual learning in online scenario aims to learn a sequence of new tasks from data stream using each data only once for training, which is more realistic than in offline mode assuming data from new task are all available. However, this problem is still under-explored for the challenging class-incremental setting in which the model classifies all classes seen so far during inference. Particularly, performance struggles with increased number of tasks or additional classes to learn for each task. In addition, most existing methods require storing original data as exemplars for knowledge replay…
CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion
Technical University of Munich（慕尼黑技术大学）；TUM School of Computation, Information and Technology（TUM计算、信息与技术学院）；Department of Computer Engineering, Learning Systems and Robotics Lab（计算机工程系、学习系统与机器人实验室）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所（MIRMI））；Robotics Institute Germany（德国机器人研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）
AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting
The University of Melbourne（墨尔本大学）；Fortemedia Singapore（Fortemedia新加坡）；Nanyang Technological University（南洋理工大学）；South China University of Technology（华南理工大学）
TS-ACL: Closed-Form Solution for Time Series-oriented Continual Learning
Central South University（中南大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Zhengzhou University（郑州大学）；The University of Hong Kong（香港大学）；Peking University（北京大学）；University of Maryland（马里兰大学）；South China University of Technology（华南理工大学）
Xi’an Jiaotong University（西安交通大学）；Singapore Management University（新加坡管理大学）；Harbin Institute of Technology（哈尔滨工业大学）；Pengcheng Laboratory（鹏城实验室）
State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i.e., domain increment learning (DIL). The key idea of the paradigm is to learn prompts independently across domains with pre-trained transformers, avoiding the use of exemplars that commonly appear in conventional methods. This results in a win-win game where the prompting can…
University of Cambridge（剑桥大学）；University of Southampton（南安普顿大学）；University of Helsinki（赫尔辛基大学）；HKUST（香港科技大学）
Continual learning approaches help deep neural network models adapt and learn incrementally by trying to solve catastrophic forgetting. However, whether these existing approaches, applied traditionally to image-based tasks, work with the same efficacy to the sequential time series data generated by mobile or embedded sensing systems remains an unanswered question. To address this void, we conduct the first comprehensive empirical study that quantifies the performance of three predominant continual learning schemes (i.e., regularization, replay, and replay with examples) on six datasets from th…
PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning
STEM University of South Australia（南澳大利亚STEM大学）；Institute for Infocomm Research, A*STAR & IPAL（信息与通信研究所，A*STAR及IPAL）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；University of Moratuwa（莫拉图瓦大学）；Toyota Motor Europe（丰田汽车欧洲公司）
With the success of pretraining techniques in representation learning, a number of continual learning methods based on pretrained models have been proposed. Some of these methods design continual learning mechanisms on the pre-trained representations and only allow minimum updates or even no updates of the backbone models during the training of continual learning. In this paper, we question whether the complexity of these models is needed to achieve good performance by comparing them to a simple baseline that we designed. We argue that the pretrained feature extractor itself can be strong enou…
From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity
School of Software, Shandong University（山东大学软件学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Imperial College London（帝国理工学院）；University of Bayreuth（拜罗伊特大学）；University of Vienna（维也纳大学）；Universite Paris-Saclay（巴黎萨克雷大学）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）；UVSQ（凡尔赛大学）；Reality Labs；University of Bordeaux（波尔多大学）；Cogitat Ltd.（Cogitat有限公司）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；National and Kapodistrian University of Athens（雅典国立卡波季斯特里安大学）；DCS Corporation（DCS公司）；DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Polish National Institute for Machine Learning (OPIUM)（波兰国家机器学习研究所（OPIUM））
Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is because current methods cannot generalise well across human subjects' data and handle learning from different heterogeneously collected data sets, thus limiting the scale of training data. On the other side, developments in transfer learning would benefit significantly from a real-world benchmark with immediate practical application. Therefore, we pick electroencephalography (EEG) as an exemplar for what makes biosignal…
Retrospective Feature Estimation for Continual Learning
VinUni-Illinois Smart Health Center（VinUni-伊利诺伊州立健康中心）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；The Chinese University of Hong Kong（香港中文大学）；Simular Research（Simular研究公司）；University of Warwick（沃里克大学）
ShanghaiTech University（上海科技大学）；University of Electronic Science and Technology of China（电子科技大学）；The University of Hong Kong（香港大学）
In clinical practice, a segmentation network is often required to continually learn on a sequential data stream from multiple sites rather than a consolidated set, due to the storage cost and privacy restriction. However, during the continual learning process, existing methods are usually restricted in either network memorizability on previous sites or generalizability on unseen sites. This paper aims to tackle the challenging problem of Synchronous Memorizability and Generalizability (SMG) and to simultaneously improve performance on both previous and unseen sites, with a novel proposed SMG-l…
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）
TaskVAE: Task-Specific Variational Autoencoders for Exemplar Generation in Continual Learning for Human Activity Recognition
Univ. Grenoble Alpes, CNRS, Grenoble INP（格拉诺布尔大学）；Institute of Engineering Univ. Grenoble Alpes（格拉诺布尔大学工程学院）；School of Computing and Engineering, Univ. of Huddersfield（赫德瑟尔菲尔德大学计算机与工程学院）
