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.

4 papers of 8,653Sort Recent · Most cited
  1. 2024
    Class Incremental Learning for Light-Weighted NetworksZhe Tao, Lu Yu, Hantao Yao … Changsheng XuIEEE TCSVT · Tianjin University of Technology · Chinese Academy of Sciences · +2
  2. 2024
    A cognition-driven framework for few-shot class-incremental learningXuan Wang, Zhong Ji, Yanwei Pang, Yunlong YuNeurocomputing · Tianjin University of Technology · Tianjin University · +3
  3. 2024
    Spatiotemporal Orthogonal Projection Capsule Network for Incremental Few-Shot Action RecognitionYangbo Feng, Junyu Gao, Changsheng XuIEEE Trans. Multimedia · Tianjin University of Technology · Chinese Academy of Sciences · +5
  4. 2024
    Camera-Incremental Object Re-Identification With Identity Knowledge EvolutionHantao Yao, Jifei Luo, Lu Yu, Changsheng XuIEEE Trans. Multimedia · Chinese Academy of Sciences · Institute of Automation · +2
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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.