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
    ComAdPro: compositional learning with prototype adaptation for logo few-shot class-incremental recognition (ChinaMM 2025)Jianxin Zhan, 陈文泰, Sujuan Hou, Weiqing MinMultimedia Systems · Shandong Normal University · Chinese Academy of Sciences · +1
  2. 2026
    CGR: calibrating generative replay for exemplar-free class-incremental learningXingcheng Zhu, Kai Han, Xiaocheng Hu … Yang LiuMultimedia Systems · Jiangsu University
  3. 2025
    Uncertainty-guided recurrent prototype distillation for graph few-shot class-incremental learningNing Zhu, Shaofan Wang, Yanfeng Sun, Baocai YinMultimedia Systems · Beijing University of Technology · Beijing Information Science & Technology University
  4. 2024
    Overcomplete-to-sparse representation learning for few-shot class-incremental learningMengying Fu, Binghao Liu, Ma Tianren, Qixiang YeMultimedia Systems · University of Chinese Academy of Sciences
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.