Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

4 papers of 6,984Sort Recent · Most cited
  1. 2026
    Diagnosing BN discrepancy in rehearsal-based class incremental learningMinghao Zhou, Quanziang Wang, Renzhen Wang … Deyu MengInternational Journal of Machine Learning and Cybernetics
  2. 2026
    Continual fine-tuning with model soup for human activity recognitionSean Yonathan Tanjung, B. Yahya, Seok-Lyong LeeInternational Journal of Machine Learning and Cybernetics
  3. 2022
    SCMP-IL: an incremental learning method with super constraints on model parametersJidong Han, Zhaoying Liu, Yujian Li, Ting ZhangInternational Journal of Machine Learning and Cybernetics · Beijing University of Technology · Guilin University of Electronic Technology
  4. 2021
    Incremental sequential three-way decision based on continual learning networkHongyuan Li, Hong Yu, Hong Yu … Huaxiong LiInternational Journal of Machine Learning and Cybernetics · Nanjing University · Chongqing University of Posts and Telecommunications · +2
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.