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.

7 papers of 6,984Sort Recent · Most cited
  1. 2026
    Learning Adaptive and Expandable Mixture Model for Continual LearningFei Ye, yongcheng zhong, Qihe Liu … Shijie ZhouAAAI · University of Electronic Science and Technology of China · University of York
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  2. 2026
    Parameter Merging with Gradient-Guided Supermasks in Online Continual LearningBenliu Qiu, Heqian Qiu, Lanxiao Wang … Hongliang LiAAAI · University of Electronic Science and Technology of China
  3. 2025
    Dynamic Expansion Diffusion Learning for Lifelong Generative ModellingFei Ye, Adrian G. Borş, Kun ZhangAAAI · University of Electronic Science and Technology of China · University of York · +1
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  4. 2025
    Continual Unsupervised Generative Modelling via Online Optimal TransportFei Ye, Adrian G. Borş, Kun ZhangAAAI · University of Electronic Science and Technology of China · University of York · +2
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  5. 2025
    Lifelong Scalable Generative System via Online Maximum Mean DiscrepancyFei Ye, Adrian G. BorşAAAI · University of Electronic Science and Technology of China · University of York
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  6. 2021
    Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience ReplayFan Zhou, Chengtai CaoAAAI · University of Electronic Science and Technology of China
  7. 2020
    Learning from the Past: Continual Meta-Learning with Bayesian Graph Neural NetworksYadan Luo, Zi Huang, Zheng Zhang … Yang YangAAAI · The University of Queensland · Harbin Institute of Technology · +1
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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.