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.

6 papers of 8,653Sort Recent · Most cited
  1. 2025
    A Generalized Few-Shot Object Detection Method via Extraction of Base-Novel Commonality With Memory Distillation of Category PrototypesJunchi Su, Xin Gao, Heping Lu … Qiangwei LiIEEE TCSVT · Beijing University of Posts and Telecommunications · China Electric Power Research Institute
  2. 2024
    Overcoming Spatial-Temporal Catastrophic Forgetting for Federated Class-Incremental LearningHao Yu, Xin Yang, Xin Gao … Tianrui LiACM International Conference on Multimedia · Southwestern University of Finance and Economics · Sichuan University · +2
  3. 2024
    Personalized Federated Continual Learning via Multi-Granularity PromptHao Yu, Xin Yang, Xin Gao … Tianrui LiKDD · Southwestern University of Finance and Economics · Sichuan University · +3
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  4. 2024
    FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge TransferXin Gao, Xin Yang, Hao Yu … Tianrui LiCVPR · Southwestern University of Finance and Economics · Southwest Jiaotong University
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  5. 2024
    Federated Continual Learning via Knowledge Fusion: A SurveyXin Yang, Hao Yu, Xin Gao … Tianrui LiTKDE · Southwestern University of Finance and Economics · Nanyang Technological University · +2
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  6. 2018PDF ↗
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.