Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

1,469 papers of 6,984 · showing 351–400Sort Recent · Most cited
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    Rethinking Continual Learning with Pre-Trained Models: Knowledge-Preserving ApproachMakoto Misaizu, Koshi Watanabe, Keisuke Maeda … Miki HaseyamaIEEE 14th Global Conference on Consumer Electronics (GCCE) · Hokkaido University
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    An Unlearning Framework for Continual LearningSayanta Adhikari, Vishnuprasadh Kumaravelu, P. K. SrijitharXiv
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    CoRe-KD: Contrastive Multi-Teacher and Retention-Aware Knowledge Distillation for Continual Multilingual Neural Machine TranslationLin Zhou, Degen HuangData Intelligence · Dalian University of Technology · Dalian University · +1
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    D3Net: Dual-Path Decoupling-Distillation for Adaptive Fusion in Continual Egocentric LearningChenghao Qi, Heqian Qiu, Zhaofeng Shi … Hongliang LiIEEE International Workshop on Multimedia Signal Processi… · University of Electronic Science and Technology of China
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    DBAB: A Dual-Branch Adaptive Balance Framework with Optimized Plasticity Branch for Class-Incremental LearningXinyu Chen, Heqian Qiu, Chenghao Qi … Hongliang LiIEEE International Workshop on Multimedia Signal Processi… · University of Electronic Science and Technology of China
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    OrthCal: Synergizing Orthogonal Contrastive Learning and Prototype Calibration for Few-Shot Class-Incremental LearningRuisong Dai, Hanwen Zhang, Xinyu Chen … Hongliang LiIEEE International Workshop on Multimedia Signal Processi… · University of Electronic Science and Technology of China
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    VidCLearn: A Continual Learning Approach for Text-to-Video GenerationLuca Zanchetta, Lorenzo Papa, Luca Maiano, Irene AmeriniInternational Conference on Image Analysis and Processing
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    Mixture of Noise for Pre-Trained Model-Based Class-Incremental LearningKai Jiang, Zhengyan Shi, Dell Zhang … Xuelong LiNeurIPS
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    Memory-specific E-I balance supports diverse replay and mitigates catastrophic forgettingTomáš Bárta, Tomoki FukaibioRxiv · Okinawa Institute of Science and Technology Graduate University
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    A multilingual sentiment analysis model based on continual learningKexin Xu, Yuemei Xu2025 International Symposium on Artificial Intelligence a… · Beijing Foreign Studies University
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    Towards Robust Visual Continual Learning with Multi-Prototype SupervisionXi-Wei Liu, Yu-Long Li, Yi-Chen Li … Imran RazzakICASSP
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    Prompt-adaptive continual learning: integrating prompt-based learning with reinforcement learning for robust robotic task adjustmentYuheng Fan, Yunxi Liu, Nozirov Jurabek, Tulong YinICCV · Zhejiang University of Technology · Guangdong Polytechnic Normal University
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    Cross-Modal Knowledge Distillation for Speech Large Language ModelsEnzhi Wang, Qicheng Li, Zhiyuan Tang, Yuhang JiaICASSP
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    GLAM: Efficient Continual Learning at Scale via Grouped LoRA Adapter MergingIrene Testa, Luigi Quarantiello, E N Coleman … Vincenzo LomonacoarXiv
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    Buffer-free Class-Incremental Learning with Out-of-Distribution DetectionSrishti Gupta, Daniele Angioni, Maura Pintor … Battista BiggioPattern Recognition · University of Cagliari · Sapienza University of Rome · +3
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    CIFNet: An Analytic Neural Learning Framework for Efficient and Calibrated Class-Incremental LearningAlejandro Dopico-Castro, Óscar Fontenla-Romero, Bertha Guijarro‐Berdiñas, Amparo Alonso‐BetanzosarXiv
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    Class incremental learning based on fine-tuning of pre-trained modelsZhuliang Yu, Ruidong ZhangInternational Conference on Internet of Things, Automatio… · South China University of Technology
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    A rate-dependent coreset selector for continual learning on time-varying data distributionsZilin Luo, Zichen Tian, Yaoyao Liu, Qianru SunNeurocomputing · Singapore Management University · University of Illinois Urbana-Champaign
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    Brain Generative Replay for Continual LearningJian‐Guo Zhou, Dongjun Liu, Wanzeng KongSpringer LNCS · Zhejiang Lab · Hangzhou Dianzi University
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    Class-incremental Weakly Supervised Object Localization Using a Contrastive Language-image Pre-trained ModelTae‐Hyung Lee, Byeongkeun KangJournal of Institute of Control Robotics and Systems
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    MEGG: replay via maximally extreme GGscore in incremental learning for neural recommendation modelsYunxiao Shi, Shuo Yang, Haimin Zhang … Min XuData mining and knowledge discovery
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    CIBLS-PLS: A Class-Incremental Broad Learning System With Pseudolabel-Guided Stacked StructureXin Liu, Zhaoyin Shi, Shuanghao Zhang … C. L. Philip ChenIEEE TAI · Shenzhen University · Shenzhen Technology University · +4
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    Learning Order Matters in Class-Incremental Learning for Sound Localization and DetectionRuchi Pandey, Manjunath Mulimani, Archontis Politis, Annamaria MesarosEuropean Signal Processing Conference (EUSIPCO) · Tampere University of Applied Sciences · Tampere University
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    Genesis: A Spiking Neuromorphic Accelerator With On-chip Continual LearningVedant Karia, Abdullah M. Zyarah, Dhireesha KudithipudiarXiv
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    Enhancing long-term memory in federated class continual learning with lightweight adaptersPan Wang, Wang Ji, Zhengyi Zhong … Jianguo ChenNeurocomputing · National University of Defense Technology · Sun Yat-sen University
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    ArcMemo: Abstract Reasoning Composition with Lifelong LLM MemoryMatthew Ho, Si Chen, Feng, Zhaoxiang … Lianhui QinarXiv
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    Few-Shot Class-Incremental Learning With Non-IID Decentralized DataCuiwei Liu, Shijie Xu, Huaijun Qiu … Liang ZhaoIEEE Internet of Things Journal · Shenyang Aerospace University · University of Electro-Communications
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    Towards Incremental Learning in Large Language Models: A Critical ReviewMladjan Jovanovic, Peter VoßExpert Systems · Singidunum University · Koo & Associates International (United States)
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    RehearMixup: Improving rehearsal-based continual learningYan Zhang, Kaiyuan Qi, Dong Wu … Yilong YinNeurocomputing · Shandong University · Inspur (China)
About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.