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 7,070Sort Recent · Most cited
  1. 2025
    FeTT: Class-Incremental Learning with Feature Transformation TuningSunyuan Qiang, Yanyan LiangMathematics · Macau University of Science and Technology
  2. 2025
    Collaborative Adapter Experts for Class-Incremental LearningSunyuan Qiang, Xinxing Yu, Yanyan Liang … Du ZhangIEEE Signal Processing Letters · Macau University of Science and Technology · Chinese Academy of Sciences · +1
  3. 2024
    Adapt and Refine: A Few-Shot Class-Incremental Learner via Pre-Trained ModelsSunyuan Qiang, Xiong Zhu, Yanyan Liang … Du ZhangSpringer LNCS · Macau University of Science and Technology · Chinese Academy of Sciences · +2
  4. 2023
    Mixture Uniform Distribution Modeling and Asymmetric Mix Distillation for Class Incremental LearningSunyuan Qiang, Jiayi Hou, Jun Wan … Du ZhangAAAI · Macau University of Science and Technology · Lafayette College · +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. It lists only 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. 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.