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.

12 papers of 6,984Sort Recent · Most cited
  1. 2025
    A Survey of Continual Reinforcement LearningChaofan Pan, Xin Yang, Yanhua Li … Jiye LiangarXiv
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  4. 2025
    Reducing the stability gap for continual learning at the edge with class balancingWei Wei, Matthias Hutsebaut-Buysse, Tom De Schepper, Kevin MetsESANN 2025 proceesdings · Department of Physics, Mathematics and Informatics · Imec the Netherlands · +4
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  5. 2024
    Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action RecognitionWei Wei, Tom De Schepper, Kevin MetsVISIGRAPP : VISAPP · University of Antwerp
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  6. 2023
    Continual Learning for Anthropomorphic Hand GraspingWanyi Li, Wei Wei, Peng WangIEEE TCDS · Chinese Academy of Sciences · Institute of Automation · +3
  7. 2023
    Neuro-inspired continual anthropomorphic graspingWanyi Li, Wei Wei, Peng WangiScience · Chinese Academy of Sciences · Institute of Automation · +3
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  8. 2022
    Balancing Between Forgetting and Acquisition in Incremental Subpopulation LearningMingfu Liang, Jiahuan Zhou, Wei Wei, Ying WuECCV · Northwestern University · Peking University
  9. 2021
    FLAR: A Unified Prototype Framework for Few-sample Lifelong Active RecognitionLei Fan, Peixi Xiong, Wei Wei, Ying WuICCV · Northwestern University
  10. 2020
    Mitigating Forgetting in Online Continual Learning via Instance-Aware ParameterizationHung-Jen Chen, An-Chieh Cheng, Da-Cheng Juan … Min SunNeurIPS
  11. 2021
    Overcoming Catastrophic Forgetting by Bayesian Generative RegularizationPatrick H. Chen, Wei Wei, Cho‐Jui Hsieh, Bo DaiICML · University of California, Los Angeles
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  12. 2019
    Overcoming Catastrophic Forgetting by Generative RegularizationPatrick H. Chen, Wei Wei, Cho-Jui Hsieh, Bo DaiarXiv
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.