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.

6 papers of 8,653Sort Recent · Most cited
  1. 2026
    Few-shot Class-Incremental Learning via Generative Co-Memory RegularizationKexin Bao, Yong Li, Dan Zeng, Shiming GeInternational Journal of Computer Vision
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  2. 2025
    Seeing 3D Through 2D Lenses: 3D Few-Shot Class-Incremental Learning via Cross-Modal Geometric RectificationTuo Xiang, Xuemiao Xu, Bangzhen Liu … Shengfeng HeICCV · South China University of Technology · Singapore Management University
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  3. 2025
    CD^2: Constrained Dataset Distillation for Few-Shot Class-Incremental LearningKexin Bao, Daichi Zhang, Hansong Zhang … Shiming GeIJCAI · Chinese Academy of Sciences · Institute of Information Engineering · +1
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  4. 2024
    Towards Personalized Federated Learning via Comprehensive Knowledge DistillationPengju Wang, Bochao Liu, Weijia Guo … Shiming GeIEEE International Conference on Systems, Man, and Cybern… · Chinese Academy of Sciences · Institute of Information Engineering
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  5. 2024
    Fusion of Current and Historical Knowledge for Personalized Federated LearningPengju Wang, Bochao Liu, Weijia Guo … Shiming GeIJCNN · Institute of Information Engineering · University of Chinese Academy of Sciences
  6. 2023
    Class-Incremental Learning Based on Anomaly DetectionLijuan Zhang, Xiaokang Yang, Kai Zhang … Dongming LiIEEE Access · Changchun University of Technology · Jilin University of Finance and Economics · +1
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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 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.