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.

5 papers of 7,070Sort Recent · Most cited
  1. 2026
    NExplore: Exploration with Neural Fields for Autonomous Scene Reconstruction.Zike Yan, Zijia Kuang, Yuetao Li … Hongbin ZhaTPAMI · Tsinghua University · Beijing Academy of Artificial Intelligence
  2. 2023
    Task-Distributionally Robust Data-Free Meta-LearningZixuan Hu, Yongxian Wei, Li Shen … Dacheng TaoTPAMI · Nanyang Technological University · Tsinghua–Berkeley Shenzhen Institute · +6
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  3. 2025
    MoE-Adapters++: Toward More Efficient Continual Learning of Vision-Language Models Via Dynamic Mixture-of-Experts AdaptersJiazuo Yu, Zichen Huang, Yunzhi Zhuge … You HeTPAMI · Dalian University of Technology · University of Electronic Science and Technology of China · +2
  4. 2025
    HiDe-PET: Continual Learning via Hierarchical Decomposition of Parameter-Efficient TuningLiyuan Wang, Jingyi Xie, Xingxing Zhang … Jun ZhuTPAMI · Tsinghua University
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  5. 2024
    A Comprehensive Survey of Continual Learning: Theory, Method and ApplicationLiyuan Wang, Xingxing Zhang, Hang Su, Jun ZhuTPAMI · Tsinghua University
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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 written 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.