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.

8 papers of 8,653Sort Recent · Most cited
  1. 2024
    Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer LearningL. Frati, Neil Traft, Jeff Clune, Nick CheneyFrontiers · University of Vermont · Canadian Institute for Advanced Research · +2
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  2. 2024
    Informed Spectral Normalized Gaussian Processes for Trajectory PredictionChristian Schlauch, Christian Wirth, Nadja KleinFrontiers · Continental (Germany) · Humboldt-Universität zu Berlin · +2
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  3. 2024
    Learning After Learning: Positive Backward Transfer in Continual LearningW. Wong, Yun Sing Koh, Gillian DobbieFrontiers · University of Auckland
  4. 2024
    Natural Mitigation of Catastrophic Interference: Continual Learning in Power-Law Learning EnvironmentsAtith Gandhi, Raj Sanjay Shah, Vijay Marupudi, Sashank VarmaFrontiers · Georgia Institute of Technology
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  5. 2024
    Bridging Continual Learning of Motion and Self-Supervised RepresentationsMatteo Tiezzi, Simone Marullo, Alessandro Betti … Stefano MelacciFrontiers · Italian Institute of Technology · University of Florence · +2
  6. 2024
    Prompt-Based Domain Incremental Learning with Modular Classification LayerBoyu Wang, Yue Ma, Qinru QiuFrontiers · Syracuse University
  7. 2024
    Expandable-RCNN: toward high-efficiency incremental few-shot object detectionYiting Li, Sichao Tian, Haiyue Zhu … Prahlad VadakkepatFrontiers · National University of Singapore · Chinese Academy of Medical Sciences & Peking Union Medical College · +3
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  8. 2024
    Context association in pyramidal neurons through local synaptic plasticity in apical dendritesMaximilian Baronig, Robert LegensteinFrontiers · Graz University of 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. 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.