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.

6 papers of 7,070Sort Recent · Most cited
  1. 2025
    Enhancing Online Continual Learning with Plug-and-Play State Space Model and Class-Conditional Mixture of DiscretizationSihao Liu, Yibo Yang, Xiaojie Li … Bernard GhanemCVPR · Harbin Institute of Technology · King Abdullah University of Science and Technology · +1
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  2. 2023
    Continual Detection Transformer for Incremental Object DetectionYaoyao Liu, Bernt Schiele, Andrea Vedaldi, Christian RupprechtCVPR · Max Planck Institute for Informatics · University of Oxford
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  3. 2023
    Computationally Budgeted Continual Learning: What Does Matter?Ameya Prabhu, Hasan Abed Al Kader Hammoud, Puneet K. Dokania … Adel BibiCVPR · University of Oxford · King Abdullah University of Science and Technology
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  4. 2023
    Real-Time Evaluation in Online Continual Learning: A New HopeYasir Ghunaim, Adel Bibi, Kumail Alhamoud … Bernard GhanemCVPR · King Abdullah University of Science and Technology · University of Oxford
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  5. 2022
    Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental LearningYujun Shi, Kuangqi Zhou, Jian Liang … Vincent Y. F. TanCVPR · National University of Singapore · Chinese Academy of Sciences · +1
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  6. 2006
    Incremental learning of object detectors using a visual shape alphabetAndreas Opelt, Axel Pinz, Andrew ZissermanCVPR · Graz University of Technology · University of Oxford
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 written 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.