Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 11,817Sort Recent · Most cited
  1. 2025
    Continual Learning on CLIP via Incremental Prompt Tuning with Intrinsic Textual AnchorsHaodong Lu, Xinyu Zhang, Kristen Moore … Dong GongTrans. Mach. Learn. Res.
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  2. 2024
    Premonition: Using Generative Models to Preempt Future Data Changes in Continual LearningMark D. McDonnell, Dong Gong, Ehsan Abbasnejad, Anton van den HengelarXiv
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  3. 2023
    RanPAC: Random Projections and Pre-trained Models for Continual LearningMark D. McDonnell, Dong Gong, Amin Parvaneh … Anton van den HengelNeurIPS
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  4. 2022
    Learning Bayesian Sparse Networks with Full Experience Replay for Continual LearningQingsen Yan, Dong Gong, Yuhang Liu … Qinfeng ShiCVPR · Australian Centre for Robotic Vision · The University of Adelaide · +1
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  5. 2012
    Incremental Learning of 3D-DCT Compact Representations for Robust Visual TrackingXi Li, Anthony Dick, Chunhua Shen … Hanzi WangTPAMI · The University of Adelaide · Xiamen University
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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.