Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 8,653Sort Recent · Most cited
  1. 2026
    AMAF-FCL: Adaptive Multi-Factor Accurate Forgetting for Heterogeneous Federated Continual LearningWeimeng Wang, Guoping Fu, Weiqiao Zhu … Xiaolin ChangApplied Sciences · China Academy of Railway Sciences · China Railway Group (China) · +1
  2. 2026
    CGRD: An Exemplar-Free Extension of Knowledge Distillation for Class-Incremental 3D Point Cloud Semantic SegmentationLei Wang, Rongxiang LiuApplied Sciences · East China University of Technology
  3. 2026
    Dynamic LoRA-Experts and Prototype-Ensemble Matching for Class-Incremental LearningHongwei Zhao, Rui Liu, Ying LiuApplied Sciences · Beihang University
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  4. 2026
    CoDC: Unified Diffusion and Classification for Enhanced Class-Incremental LearningJunli Chen, Jianming Wen, Sijin Wang, Qiuyu ZhuApplied Sciences · Shanghai University
  5. 2026PDF ↗
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.