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.

6 papers of 6,984Sort Recent · Most cited
  1. 2026
    Complementary Asymmetric Representation Learning for Exemplar-Free Class-Incremental LearningRunhang Chen, Xiao‐Yuan Jing, Xiaodong JiaACM Transactions · Wuhan University · Henan University of Engineering · +2
  2. 2026
    Leveraging Textual Semantic Guidance for Few-Shot Class-Incremental LearningYuqiao Xu, Hantao Yao, Lu Yu, Changsheng XuACM Transactions · Tianjin University of Technology · University of Science and Technology of China · +1
  3. 2026
    Random Dense Knowledge Distillation for Continual LearningJie Chu, Pei Liu, Tong Su … Zenglin ShiACM Transactions · Hefei University of Technology · Zhengzhou University
  4. 2026
    Towards Experience Replay for Class-Incremental Learning in Fully-Binary NetworksYanis Basso-Bert, Anca Molnos, Romain Lemaire … Antoine DupretACM Transactions · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · CEA Grenoble · +3
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  5. 2026
    Orchestrating Prompt Expertise: Enhancing Knowledge Distillation via Expert-Guided TuningXu Meng, Jun Rao, Shuhan Qi … Xuan WangACM Transactions · Harbin Institute of Technology · Global Security Intelligence (United Kingdom) · +1
  6. 2026
    Text-Prompted Prompt Generator with Uncertainty Regularization for Rehearsal-Free Class-Incremental LearningShaofan Wang, Fuhao Wei, Hong Ma … Baocai YinACM Transactions · Beijing University of Technology
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.