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.

10 papers of 11,817Sort Recent · Most cited
  1. 2026
    AdaptCMVC++: Robust and Flexible Adaptation to Incremental Views in Continual Multi-view Clustering.Jing Wang, Songhe Feng, Jiacheng Li … Michael C. KampffmeyerTPAMI
  2. 2026
    Kwai Keye-VL-2.0 Technical ReportKwai Keye Team, Bin Wen, Changyi Liu … Ruilin ZhangarXiv
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  3. 2026
    Semantic-aware replay and key knowledge fusion for 3D continual learningLongyue Qian, Lin Zhang, Jing Wang … Mingxin ZhangBiomimetic Intelligence and Robotics
  4. 2025
    AdaptCMVC: Robust Adaption to Incremental Views in Continual Multi-view ClusteringJing Wang, Songhe Feng, Kristoffer Wickstrøm, Michael C. KampffmeyerCVPR
  5. 2025
    Efficient Visual Region Recognition in the Open World ScenariosJing Wang, Yonghua CaoJournal of Computer Science and Artificial Intelligence
  6. 2024PDF ↗
  7. 2024
    Life Control Based on Limited Incremental learning and WE-OSELMYudi Wen, Jing Wang, Ying ZhangInternational Conference on Computer, Vision and Intellig…
  8. 2024
    Meta-reinforcement Learning Task Planning Based on Improved Curriculum Learning SamplingWen Zhang, Jing Wang, Ning WanACM Cloud and Autonomic Computing Conference
  9. 2023
    Research on the application of Neural Network Model in Knowledge graph completion technologyZibo Yang, Yali Qi, Zixuan Li … Jing WangInternational Conference on Educational Knowledge and Inf…
  10. 2023
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.