Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 8,653Sort Recent · Most cited
  1. 2026
    Release the Potential of Memory Buffer in Continual Learning: A Dynamic System PerspectiveZhenyi Wang, Li Shen, Tiehang Duan … Dacheng TaoTPAMI · University of Central Florida · Artificial Intelligence in Medicine (Canada) · +6
  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
    IL-NeRF: Incremental Learning for Neural Radiance Fields with Camera Pose AlignmentLetian Zhang, Ming Li, Chen Chen, Jie XuCVPR · Middle Tennessee State University · University of Central Florida · +1
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  4. 2023
    Improving Replay Sample Selection and Storage for Less Forgetting in Continual LearningDaniel Brignac, Niels da Vitoria Lobo, Abhijit MahalanobisICCV · University of Arizona · University of Central Florida
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  5. 2022
    CNLL: A Semi-supervised Approach For Continual Noisy Label LearningNazmul Karim, Umar Khalid, Ashkan Esmaeili, Nazanin RahnavardCVPR · University of Central Florida
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  6. 2020
    iTAML: An Incremental Task-Agnostic Meta-learning ApproachJathushan Rajasegaran, Salman Khan, Munawar Hayat … Mubarak ShahCVPR · Inception Institute of Artificial Intelligence · Linköping University · +1
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  7. 2018
    Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural NetworksAndrea Soltoggio, Kenneth O. Stanley, Sebastian RisiNeural Networks · Loughborough University · University of Central Florida · +1
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  8. 2018
    Deep Face Detector Adaptation Without Negative Transfer or Catastrophic ForgettingMuhammad Abdullah Jamal, Haoxiang Li, Boqing GongCVPR · University of Central Florida · Adobe Systems (United States) · +1
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. By default it shows the 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. The rest are one click away under “All papers”. 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.