Continual Learning

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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

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

Rethinking Data Imbalance in Class Incremental Surgical Instrument Segmentation
Oct 26, 2026Shifang Zhao, Long Bai, Kun Yuan +8Semantic Segmentation Medical Image Segmentation
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong · Chair for Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany · University of Strasbourg, CNRS, INSERM, ICube, Strasbourg, France +2
Task Relevance-Driven Prompt Expansion for Federated Continual Learning
Oct 25, 2026Jiahua Dong, Wenqi Liang, Yang Cong +6Continual Learning Federated Continual Learning
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · University of Trento, Trento, Italy · South China University of Technology, Guangzhou, China +4
Score-Preserving Continual Lasso under Task Shift
Oct 23, 2026Jingyi Guo, Runze Li, Lingzhou XueContinual Learning Regularization-Based Continual Learning
Pennsylvania State University
WiMoT: Replay-Free Continual Wi-Fi Sensing via Moment Transport
Oct 23, 2026Chunchun Yang, Yang Cao, Zhitang ChenContinual Learning Wi-Fi Sensing
School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China · Independent Researcher
HERD: Harness-Evolving Recursive Distillation for Continual Learning
Oct 22, 2026Xun Sun, Thanh Thi Nguyen, Thuy-Trang VuContinual Learning Continual Learning for LLM Agents
Monash University · University of the Sunshine Coast
Language Guidance for Supervised Vision Training: An Empirical Study of Generalization
Oct 19, 2026Shruthi Gowda, Nikita Jain, Elahe Arani +1OOD Generalization Shortcut Learning
Eindhoven University of Technology
Out-of-Distribution Detection in Continual Learning
Oct 19, 2026Nimeshika Udayangani, Flora D. Salim, Christopher Leckie +1OOD Detection Continual Learning
School of Computing and Information Systems, The University of Melbourne, Australia · School of Computer Science Engineering, University of New South Wales, Australia
An Impossibility Result for ETF Target Geometry, and an Equinorm Remedy for Imbalanced Analytic Continual Learning
Oct 17, 2026Berk SezerContinual Learning Class-Incremental Learning
Middle East Technical University, Türkiye
Reliable Knowledge Acquisition for Continual Learning Agents
Oct 15, 2026Yixu Huang, Jiaqi W. MaContinual Learning for LLM Agents Continual Learning
University of California, Davis · University of Illinois Urbana-Champaign
Overcoming Catastrophic Forgetting: Continual Learning for Radar Waveform Classification
Oct 13, 2026Mohamed Rabie, Chinthana J. Panagamuwa, Konstantinos G. KyriakopoulosContinual Learning Catastrophic Forgetting
Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, U.K.
Stable-Plastic Subspace Construction for Differentially Private Continual LoRA
Oct 13, 2026Jin Liu, Ning Xi, Yinbin Miao +3Continual Learning Privacy-Preserving ML
Xidian University · Tianjin University
Effect of Spherical Parameter Space on Number of Non-Forgetting Tasks and Capacity of Continual Learning
Oct 12, 2026Nazanin MirhosseiniContinual Learning Catastrophic Forgetting
Colorado State University, Fort Collins, Co, USA
Engram Neuron Inspired Adaptive Memorization for Lifelong Learning
Oct 10, 2026Suiyang Jiang, Bomin Wang, Hangqi Zhou +2Continual Learning Memory-Augmented Neural Networks
School of Data Science, Fudan University, 220 Handan Road, Shanghai, 200433, China · Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 220 Handan Road, Shanghai, 200433, China
LadderEdit: Edit-Level Residual Compression for Memory-Efficient Lifelong Editing of LLMs
Oct 8, 2026Xiaobing Yu, Peijie Qiu, Jin Yang +5Language Model Editing Memory-Efficient Fine-Tuning
Yale University · Washington University in St. Louis · Icahn School of Medicine at Mount Sinai +1
Continual Learning without Continual Training
Oct 7, 2026Nikita Narayanan, Ritham Majumdarr, Sonali ParbhooContinual Learning Class-Incremental Learning
Imperial College London
MAGIC: Topology-Aware Analytic Graph Few-Shot Class-Incremental Learning
Oct 4, 2026Junlin Chen, Yuhan Wang, Xuefei Wang +3Class-Incremental Learning Continual Learning
Beihang University, Beijing, China
Gated Target Propagation for Compositional Generalization in Continual Learning
Oct 3, 2026Abdel Mfougouon Njupoun, Colin Bredenberg, Blake Aaron Richards +1Continual Learning Compositional Generalization
Université de Montr´eal Mila Quebec AI Institute · University of Oregon Mila Quebec AI Institute · McGill University Mila Quebec AI Institute
VoiceWeaver: Staged Learning of Structured Controls for Expressive Speech and Sound-Event Generation
Oct 3, 2026Xiaosu Su, Yun Cao, Yiping Ni +1Controllable Speech Generation TTS Synthesis
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
PaLoRA: Paced Low-Rank Adaptation for Continual Learning
Oct 3, 2026Yuxuan Li, Fanhu Zeng, Hao TangContinual LoRA Continual Learning
School of Computer Science Peking University · Institute of Automation Chinese Academy of Sciences
One-Step Curvature Probes Miss the Fitting Operator: Retained Capacity and Terminal Null-Space Correction for Continual Learning
Oct 2, 2026Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Ibne Farabi Shihab, Md Najmus SwaqeebContinual Learning Incremental Learning
Department of Computer Science and Engineering, BRAC University, Bangladesh · Department of Computer Science, Iowa State University, USA · BRAC University, Bangladesh
What Should World Models Forget? Stratified Retention for Continual Adaptation
Oct 2, 2026Nishit Anand, Ramani Duraiswami, Dinesh ManochaWorld Model Learning Continual Learning
University of Maryland, College Park, USA
Task-Oriented Rank Adaptation for Continual Learning in Text Classification
Oct 1, 2026Rey Sanchez Lopez, Eduardo Morales Manzanares, Hugo Jair EscalanteContinual Learning Low-Rank Adaptation
INAOE, Puebla, Mexico · The University of Texas at El Paso, El Paso, TX, USA
MuLoRA: Spectrally Balanced Low-Rank Adaptation for Continual Learning
Oct 1, 2026Junkang LiuContinual Learning Low-Rank Adaptation
Tianjin University
CLEAN: Psychometrically Consistent Incremental Cognitive Diagnosis under Concept-Space Expansion via Architectural Isolation
Oct 1, 2026Tao He, Jinxing Xiang, Fan JiangContinual Learning Catastrophic Forgetting
Faculty of Education, Shenzhen University Shenzhen, China · School of Education Science, Guangdong Polytechnic Normal University Guangzhou, China
Dynamic LoRA-Experts and Prototype-Ensemble Matching for Class-Incremental Learning
Sep 30, 2026Hongwei Zhao, Rui Liu, Yansong LiuClass-Incremental Learning Prototype-Based Classification
School of Computer Science and Engineering, Beihang University
SE-ADD: Self-Evolving Audio Deepfake Detection with Mistake-Driven Supervision
Sep 30, 2026Rong Wan, Wei Xie, Jiaxi Li +4Audio Deepfake Detection Self-Training for Language Models
University of Surrey · Guangxi University · Shenzhen University of Advanced Technology +1
Hyperbolic Prototype Routing for Rehearsal-Free Class-Incremental Learning
Sep 30, 2026HongWei Zhao, Rui Liu, Yong ChenClass-Incremental Learning Prototype-Based Classification
Beihang University · Beijing University of Posts and Telecommunications
A Dynamical Theory of LoRA in Continual Learning
Sep 30, 2026Théo Marchetta, Filippo Alessandroni, Alessandro Breccia +2Continual Learning Low-Rank Adaptation
Department of Mathematics, Alma Mater Studiorum – Università di Bologna, Piazza di Porta San Donato 5, 40126 Bologna, Italy · Gatsby Computational Neuroscience Unit, University College London · Donders Centre for Neuroscience, Radboud University, Nijmegen, The Netherlands
Generalized Residual Closure: General Learning Dynamics for Stability-Plasticity Compatibility
Sep 30, 2026Dongxu Li, Yinuo Zhang, Hongyu Zhang +1Stability-Plasticity Dilemma Continual Learning
Xi’an Jiaotong University · Xi’an University of Architecture and Technology
Continual Learning of Dynamical Systems in Recurrent Neural Networks through Recyclable Unit Gating
Sep 29, 2026Sima Hashemi, Daniel Durstewitz, Georgia KoppeDynamical Systems Continual Learning
Faculty of Mathematics and Computer Science, Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany · Department of Theoretical Neuroscience, Central Institute of Mental Health (CIMH), Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany · Hector Institute for AI in Psychiatry & Department of Psychiatry and Psychotherapy, CIMH, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany +1

Oct 26, 2026J/K move · Enter open · S save

Rethinking Data Imbalance in Class Incremental Surgical Instrument Segmentation

Shifang Zhao, Long Bai, Kun Yuan, Feng Li+7

Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong · Chair for Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany · University of Strasbourg, CNRS, INSERM, ICube, Strasbourg, France+2

Semantic Segmentation Medical Image Segmentation Continual Learning Class-Imbalanced Learning

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In surgical instrument segmentation, the increasing variety of instruments over time poses a significant challenge for existing neural networks, as they are unable to effectively learn such incremental tasks and suffer from catastrophic forgetting. When learning new data, the model experiences a sharp performance drop on previously learned data. Although several continual learning methods have been proposed for incremental understanding tasks in surgical scenarios, the issue of data imbalance often leads to a strong bias in the segmentation head, resulting in poor performance. Data imbalance can occur in two forms: (i) class imbalance between new and old data, and (ii) class imbalance within the same time point of data. Such imbalances often cause the dominant classes to take over the training process of continual semantic segmentation (CSS). To address this issue, we propose SurgCSS, a novel plug-and-play CSS framework for surgical instrument segmentation under data imbalance. Specifically, we generate realistic surgical backgrounds through inpainting and blend instrument foregrounds with the generated backgrounds in a class-aware manner to balance the data distribution in various scenarios. We further propose the Class Desensitization Loss by employing contrastive learning to correct edge biases caused by data imbalance. Moreover, we dynamically fuse the weight parameters of the old and new models to achieve a better trade-off between the biased and unbiased model weights. To investigate the data imbalance problem in surgical scenarios, we construct a new benchmark for surgical instrument CSS by integrating datasets. Extensive experiments demonstrate the effectiveness of the proposed framework, achieving significant performance improvement against existing baselines.