Continual Learning

Follow this topic Ask about this topic

Continual Learning studies how models adapt to sequences of tasks or non-stationary data without losing useful earlier knowledge, a challenge known as catastrophic forgetting. Common approaches replay selected examples, regularize updates to protect important parameters, isolate tasks with adapters or separate subnetworks, or manage knowledge through prompts, memory, and model expansion. Methods are tested on class- and domain-incremental image benchmarks such as CIFAR and ImageNet, as well as task-free streams and applications in language, robotics, and healthcare. A central tension is balancing retention with the capacity to learn, while evaluations increasingly question assumptions such as known task boundaries and seek to measure transfer, recovery, resource use, and performance under realistic shifts.

Latest papers

304 papers35 papers in the last four weeks, up 84% on the four weeks before. 0.2% of all new papers.

IDER: IDempotent Experience Replay for Reliable Continual Learning
Feb 28, 2026Zhanwang Liu, Yuting Li, Haoyuan Gao +4Continual Learning Catastrophic Forgetting
School of Computer Science, Shanghai Jiao Tong University · State Key Laboratory of General Artificial Intelligence, BIGAI · Lehigh University +1
Task-Driven Subspace Decomposition for Knowledge Sharing and Isolation in LoRA-based Continual Learning
Feb 27, 2026Lingfeng He, De Cheng, Huaijie Wang +3Continual Learning Low-Rank Adaptation
State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi’an, China · School of Electronic Engineering, Xidian University, Xi’an, China · AUTHORERR: Missing \icmlaffiliation.
SegReg: Latent Space Regularization for Improved Medical Image Segmentation
Feb 26, 2026Puru Vaish, Amin Ranem, Felix Meister +3Latent-Space Regularization Medical Image Segmentation
Department of Applied Mathematics, Technical Medical Centre, University of Twente, The Netherlands · Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany
Tell Me What To Learn: Generalizing Neural Memory to be Controllable in Natural Language
Feb 26, 2026Max S. Bennett, Thomas P. Zollo, Richard ZemelMemory-Augmented Neural Networks Lifelong Learning Agents
Columbia University
$φ$ -DPO: Fairness Direct Preference Optimization Approach to Continual Learning in Large Multimodal Models
Feb 26, 2026Thanh-Dat Truong, Huu-Thien Tran, Jackson Cothren +2Continual Learning Direct Preference Optimization
CVIU Lab, University of Arkansas, USA · Dep. of Geosciences, University of Arkansas, USA · Carnegie Mellon University, USA
Learning in the Null Space: Small Singular Values for Continual Learning
Feb 25, 2026Cuong Anh Pham, Praneeth Vepakomma, Samuel HorváthContinual Learning Orthogonal Representation Learning
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), UAE · Massachusetts Institute of Technology (MIT), USA
UniWhisper: Efficient Continual Multi-task Training for Robust Universal Audio Representation
Feb 25, 2026Yuxuan Chen, Peize He, Haoyuan Yu +1Audio Representation Learning Audio Understanding
Jilin University · University of Electronic Science and Technology of China · Hunan University +1
PowerModelsGAT-AI: Physics-Informed Graph Attention for Multi-System Power Flow with Continual Learning
Feb 24, 2026Chidozie Ezeakunne, Jose E. Tabarez, Reeju Pokharel +1Physics-Informed GNNs Power Systems
Los Alamos National Laboratory, Los Alamos, NM, USA · Department of Physics, University of Central Florida, Orlando, FL 32816 USA
Exploring the Impact of Parameter Update Magnitude on Forgetting and Generalization of Continual Learning
Feb 24, 2026JinLi He, Liang Bai, Xian YangContinual Learning Catastrophic Forgetting
Institute of Intelligent Information Processing, Shanxi University, Taiyuan, 030006, China · Alliance Manchester Business School, The University of Manchester, Manchester, M13 9PL, UK
Understanding the Role of Rehearsal Scale in Continual Learning under Varying Model Capacities
Feb 24, 2026JinLi He, Liang Bai, Xian YangContinual Learning Replay-Based Continual Learning
Institute of Intelligent Information Processing, Shanxi University, Taiyuan, 030006, China · Alliance Manchester Business School, The University of Manchester, Manchester, M13 9PL, UK
Representation Stability in a Minimal Continual Learning Agent
Feb 23, 2026Vishnu SubramanianContinual Learning Lifelong Learning Agents
AI/ML Computational Science Associate Manager, Accenture
Active perception and disentangled representations allow continual, episodic zero and few-shot learning
Feb 22, 2026David Rawlinson, Gideon KowadloContinual Learning Few-Shot Learning
Cerenaut AI Melbourne Australia
CARL-CXR: Continual Adapter-Based Routing for Task-Unknown Chest Radiograph Classification
Feb 17, 2026Muthu Subash Kavitha, Anas Zafar, Amgad Muneer +1Task-Incremental Learning Task-Free Continual Learning
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
SAILS: Segment Anything with Incrementally Learned Semantics for Task-Invariant and Training-Free Continual Learning
Feb 16, 2026Shishir Muralidhara, Didier Stricker, René SchusterTask-Free Continual Learning Continual Learning
German Research Center for Artificial Intelligence (DFKI) · RPTU – University of Kaiserslautern-Landau, Kaiserslautern
Spectral Imbalance Causes Forgetting in Low-Rank Continual Adaptation
Jan 31, 2026Hao Gu, Mao-Lin Luo, Zi-Hao Zhou +3VLM Adaptation Continual Learning
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China · Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China
Knowledge-Aware Evolution for Task-Free Streaming Federated Continual Learning with Arbitrary Class Overlap
Jan 27, 2026Sixing Tan, Xianmin LiuContinual Learning Federated Continual Learning
Faculty of Computing Harbin Institute of Technology
Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning
Jan 24, 2026Fatema Siddika, Md Anwar Hossen, Tanwi Mallick +1Continual Learning for LLMs Continual Learning
Iowa State University, Ames, USA · Argonne National Laboratory, USA
The Urysohn Ladder: Recursive Metric Contraction for Scalable Continual Learning
Dec 20, 2025Xin LiContinual Learning Hierarchical Representation Learning
Department of Computer Science University at Albany Albany, NY 12222
Poincaré Meets Bellman: Revisable Memory, Operational Quotients, and Evidence-Supported Learning in Changing Environments
Nov 28, 2025Xin LiMarkov Decision Processes Dynamic Programming
Department of Computer Science University at Albany
Are Current Continual Learning Methods Truly Agnostic? Introducing OPRE, a Step Toward Agnostic Continual Learning
Nov 11, 2025Raphaël Bayle, Martial Mermillod, Robert M. FrenchContinual Learning Coreset Selection
Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, Grenoble, France · LEAD-CNRS UMR 5022, U. de Bourgogne Europe, Dijon, France
Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization
Oct 8, 2025Kanglei Zhou, Qingyi Pan, Xingxing Zhang +4Continual Learning Action Quality Assessment
Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China · Department of Statistics and Data Science, Tsinghua University, Beijing 100084, China · Department of Computer Science and Technology, Institute for AI, BNRist Center, Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University +3
UniPrompt-CL: Efficient Single-Inference Continual Learning for Medical Image Classification
Aug 14, 2025Gyutae Oh, Jitae ShinContinual Learning Domain-Incremental Learning
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
Dynamic gain neuromodulation attenuates stability gaps during continual joint training
Jul 18, 2025Alejandro Rodriguez-Garcia, Anindya Ghosh, Srikanth RamaswamyContinual Learning Incremental Learning
Neural Circuits Laboratory, Biosciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
The Bayesian Approach to Continual Learning: An Overview
Jul 11, 2025Tameem AdelTask-Incremental Learning Class-Incremental Learning
NPL, Maxwell Centre, University of Cambridge, JJ Thomson Avenue, Cambridge, CB3 OHE, United Kingdom.
SEAL: Searching Expandable Architectures for Incremental Learning
May 15, 2025Matteo Gambella, Manuel RoveriDynamic Neural Networks Continual Learning
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
Stochastic Engrams for Efficient Continual Learning
Mar 27, 2025Isabelle Aguilar, Luis Fernando Herbozo Contreras, Omid KaveheiClass-Incremental Learning Binary Neural Networks
School of Biomedical Engineering, The University of Sydney, NSW, 2006, Australia
Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics
Jan 12, 2025Jie Mei, Alejandro Rodriguez-Garcia, Daigo Takeuchi +4Continual Learning Neural Network Training Dynamics
IT:U Interdisciplinary Transformation University Austria, Linz, Austria · International Research Center for Neurointelligence, The University of Tokyo, Tokyo, Japan · Department of Anatomy, University of Quebec in Trois-Rivieres, Trois-Rivieres, QC, Canada +8
Continual Memorization of Factoids in Language Models
Nov 11, 2024Howard Chen, Jiayi Geng, Adithya Bhaskar +2Continual Learning for LLMs Memorization in Language Models
Princeton Language and Intelligence (PLI), Princeton University
iTeach: In the Wild Interactive Teaching for Failure-Driven Adaptation of Robot Perception
Oct 1, 2024Jishnu Jaykumar P, Cole Salvato, Vinaya Bomnale +4Continual Learning Human-in-the-Loop AI
The University of Texas at Dallas
Collaborative Knowledge Distillation via a Learning-by-Education Node Community
Sep 30, 2024Anestis Kaimakamidis, Ioannis Mademlis, Ioannis PitasOnline Knowledge Distillation Continual Learning
Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece

Feb 28, 2026J/K move · Enter open · S save

IDER: IDempotent Experience Replay for Reliable Continual Learning

Zhanwang Liu, Yuting Li, Haoyuan Gao, Yexin Li+3

School of Computer Science, Shanghai Jiao Tong University · State Key Laboratory of General Artificial Intelligence, BIGAI · Lehigh University+1

Continual Learning Catastrophic Forgetting Replay-Based Continual Learning

Open paper AskPDF arXiv

Catastrophic forgetting, the tendency of neural networks to forget previously learned knowledge when learning new tasks, has been a major challenge in continual learning (CL). To tackle this challenge, CL methods have been proposed and shown to reduce forgetting. Furthermore, CL models deployed in mission-critical settings can benefit from uncertainty awareness by calibrating their predictions to reliably assess their confidences. However, existing uncertainty-aware continual learning methods suffer from high computational overhead and incompatibility with mainstream replay methods. To address this, we propose idempotent experience replay (IDER), a novel approach based on the idempotent property where repeated function applications yield the same output. Specifically, we first adapt the training loss to make model idempotent on current data streams. In addition, we introduce an idempotence distillation loss. We feed the output of the current model back into the old checkpoint and then minimize the distance between this reprocessed output and the original output of the current model. This yields a simple and effective new baseline for building reliable continual learners, which can be seamlessly integrated with other CL approaches. Extensive experiments on different CL benchmarks demonstrate that IDER consistently improves prediction reliability while simultaneously boosting accuracy and reducing forgetting. Our results suggest the potential of idempotence as a promising principle for deploying efficient and trustworthy continual learning systems in real-world applications.Our code is available at https://github.com/YutingLi0606/Idempotent-Continual-Learning.