Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

12 papers of 11,817Sort Recent · Most cited
  1. 2026
    Leveraging Textual Semantic Guidance for Few-Shot Class-Incremental LearningYuqiao Xu, Hantao Yao, Lu Yu, Changsheng XuACM Transactions
  2. 2025
    Leveraging Multiple Deep Experts for Online Class-incremental LearningZhe Tao, Lu Yu, Hantao Yao, Changsheng XuIEEE International Conference on Multimedia and Expo
  3. 2025PDF ↗
  4. 2024
    Class Incremental Learning for Light-Weighted NetworksZhe Tao, Lu Yu, Hantao Yao … Changsheng XuIEEE TCSVT
  5. 2024PDF ↗
  6. 2024
    Hierarchical Prompts for Rehearsal-free Continual LearningYukun Zuo, Hantao Yao, Lu Yu … Changsheng XuarXiv
    PDF ↗
  7. 2024
    Few-shot Incremental Learning with Textual Knowledge Embedding by Visual-language ModelHantao Yao, Lu Yu, Changsheng XuInternational Journal of Software and Informatics
  8. 2023PDF ↗
  9. 2023
  10. 2023
    Camera-Incremental Object Re-Identification With Identity Knowledge EvolutionHantao Yao, Jifei Luo, Lu Yu, Changsheng XuIEEE Trans. Multimedia
    PDF ↗
  11. 2020
    Self-Training for Class-Incremental Semantic SegmentationLu Yu, Xialei Liu, Joost van de WeijerTNNLS · Tianjin University of Technology · Nankai University · +1
    PDF ↗
  12. 2020
    Semantic Drift Compensation for Class-Incremental LearningLu Yu, Bartłomiej Twardowski, Xialei Liu … Joost van de WeijerCVPR · Universitat Autònoma de Barcelona · Northwestern Polytechnical University · +1
    PDF ↗
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.