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.

7 papers of 8,653Sort Recent · Most cited
  1. 2024
    Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail AnchorHao Yu, Xin Yang, Le Zhang … Qiang YangCVPR · Southwestern University of Finance and Economics · University of Electronic Science and Technology of China · +3
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  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
    Continual Learning for Smart City: A SurveyLi Yi Yang, Zhipeng Luo, Shiming Zhang … Tianrui LiTKDE · Southwest Jiaotong University
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  5. 2024
    Open Continual Feature Selection via Granular-Ball Knowledge TransferXuemei Cao, Xin Yang, Shuyin Xia … Tianrui LiTKDE · Southwestern University of Finance and Economics · Chongqing University of Posts and Telecommunications · +1
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  6. 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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  7. 2024
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 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.