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. 2023
    Learnware: small models do bigZhihua Zhou, Zhi-Hao TanInformation Sciences · Nanjing University
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  2. 2023
    Task-specific parameter decoupling for class incremental learningRunhang Chen, Xiao‐Yuan Jing, Fei Wu … Yaru HaoInformation Sciences · Wuhan University · Guangdong University of Petrochemical Technology · +2
  3. 2023
    Online Attentive Kernel-Based Temporal Difference LearningXingguo Chen, Guang Yang, Shangdong Yang … Yang GaoKnowledge-Based Systems · Beijing Technology and Business University · Nanjing University of Posts and Telecommunications · +3
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  4. 2023
    Preserving Locality in Vision Transformers for Class Incremental LearningBowen Zheng, Da-Wei Zhou, Han-Jia Ye, De‐Chuan ZhanIEEE International Conference on Multimedia and Expo (ICME) · Nanjing University
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  5. 2023
    Adaptive Plasticity Improvement for Continual LearningYan-Shuo Liang, Wu-Jun LiCVPR · Nanjing University
  6. 2023
    PyCIL: a Python toolbox for class-incremental learningDa-Wei Zhou, Fuyun Wang, Han-Jia Ye, De‐Chuan ZhanInformation Sciences · Nanjing University
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  7. 2023
    NeCa: Network Calibration for Class Incremental LearningZhenyao Zhang, Lijun ZhangSpringer LNCS · Nanjing University
  8. 2023
    Few-Shot Class-Incremental Learning by Sampling Multi-Phase TasksDa-Wei Zhou, Han-Jia Ye, Liang Ma … De-Chuan ZhanTPAMI · Nanjing 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. 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.