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.

8 papers of 8,653Sort Recent · Most cited
  1. 2026
    SPGNet: Spectral Prototype Generation for balancing the memory-structure dilemma in graph continual learningYanfeng Sun, Binbin Chen, Shaofan WangDisplays · Beijing University of Technology
  2. 2026
    Quadruplet Augmentation With Attribute and Structure Invariance for Online Continual LearningJiamin Wu, Shaofan Wang, Yanan Sun … Baocai YinMemberTPAMI · Beijing University of Technology · Beijing Academy of Artificial Intelligence
  3. 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
  4. 2026
    Debiased Hypernetworks Are Generative Class-Incremental LearnersShaofan Wang, Pengli Guo, Weixing Wang … Baocai YinIEEE Trans. Multimedia · Beijing University of Technology
  5. 2026
    Class-Weighted Prompting for rehearsal-free class-incremental learningHong Ma, Shaofan Wang, Fuhao Wei … Baocai YinDisplays · Beijing University of Technology
  6. 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
  7. 2025
    Dual-Domain Division Multiplexer for General Continual Learning: A Pseudo Causal Intervention StrategyJian-Yong wu, Shaofan Wang, Yanfeng Sun … Qingming HuangTIP · Beijing University of Technology · University of Chinese Academy of Sciences
  8. 2025
    Dual-Attention Transformers for Class-Incremental Learning: A Tale of Two MemoriesShaofan Wang, Weixing Wang, Yanfeng Sun … Baocai YinIEEE Trans. Multimedia · Beijing University of Technology · The University of Sydney
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.