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. 2025
    PASS++: A Dual Bias Reduction Framework for Non-Exemplar Class-Incremental LearningFei Zhu, Xu-Yao Zhang, Zhen Cheng, Cheng‐Lin LiuTPAMI · Chinese University of Hong Kong, Shenzhen · Chinese Academy of Sciences · +1
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  2. 2024
    Large-scale continual learning for ancient Chinese character recognitionYue Xu, Xu-Yao Zhang, Zhaoxiang Zhang, Cheng‐Lin LiuPattern Recognition · Chinese Academy of Sciences · Institute of Automation · +1
  3. 2023
    Imitating the oracle: Towards calibrated model for class incremental learningFei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng‐Lin LiuNeural Networks · Chinese Academy of Sciences · Shandong Institute of Automation · +3
  4. 2021
    Calibration for Non-Exemplar Based Class-Incremental LearningFei Zhu, Xu-Yao Zhang, Cheng‐Lin LiuIEEE International Conference on Multimedia and Expo (ICME) · Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence · +3
  5. 2021
    Prototype Augmentation and Self-Supervision for Incremental LearningFei Zhu, Xu-Yao Zhang, Chuang Wang … Cheng‐Lin LiuCVPR · Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence · +3
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. By default it shows the 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. The rest are one click away under “All papers”. 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.