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.

11 papers of 6,984Sort Recent · Most cited
  1. 2025
    KAC: Kolmogorov-Arnold Classifier for Continual LearningYusong Hu, Zichen Liang, Fei Yang … Ming‐Ming ChengCVPR · Nankai University
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  2. 2025
    Class Incremental Learning for Image Classification With Out-of-Distribution Task IdentificationXusheng Cao, Haori Lu, Xialei Liu, Ming‐Ming ChengIEEE Trans. Multimedia · Nankai University
  3. 2024
    Reformulating Classification as Image-Class Matching for Class Incremental LearningYusong Hu, Zichen Liang, Xialei Liu … Ming‐Ming ChengIEEE TCSVT · Nankai University
  4. 2024
    Generative Multi-modal Models are Good Class-Incremental LearnersXusheng Cao, Haori Lu, Linlan Huang … Ming‐Ming ChengCVPR · Nankai University
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  5. 2024
    Task-Adaptive Saliency Guidance for Exemplar-Free Class Incremental LearningXialei Liu, Jiang-Tian Zhai, Andrew D. Bagdanov … Ming‐Ming ChengCVPR · Nankai University · University of Florence · +1
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  6. 2024PDF ↗
  7. 2023
    Class Incremental Learning with Pre-trained Vision-Language ModelsXialei Liu, Xusheng Cao, Haori Lu … Ming‐Ming ChengarXiv
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  8. 2023
    Masked Autoencoders are Efficient Class Incremental LearnersJiang-Tian Zhai, Xialei Liu, Andrew D. Bagdanov … Ming‐Ming ChengICCV · Nankai University · University of Florence · +1
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  9. 2023
    Endpoints Weight Fusion for Class Incremental Semantic SegmentationJia–Wen Xiao, Chang–Bin Zhang, Jiekang Feng … Ming‐Ming ChengCVPR · Nankai University · Tianjin University
  10. 2022
    Long-Tailed Class Incremental LearningXialei Liu, Yusong Hu, Xusheng Cao … Ming‐Ming ChengECCV
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  11. 2022
    Representation Compensation Networks for Continual Semantic SegmentationChang–Bin Zhang, Jia-wen Xiao, Xialei Liu … Ming‐Ming ChengCVPR · Nankai University · Hong Kong University of Science and Technology
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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 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.