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.

9 papers of 11,817Sort Recent · Most cited
  1. 2022
    Lifelong Reinforcement Learning with Modulating MasksEseoghene Ben-Iwhiwhu, Saptarshi Nath, Praveen K. Pilly … Andrea SoltoggioTrans. Mach. Learn. Res.
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  2. 2022
    Hierarchically branched diffusion models leverage dataset structure for class-conditional generationAlex M. Tseng, Shen, Max, Tommaso Biancalani, Gabriele ScaliaTrans. Mach. Learn. Res.
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  3. 2022
    Continual Learning by Modeling Intra-Class VariationLonghui Yu, Tianyang Hu, Lanqing Hong … Weiyang LiuTrans. Mach. Learn. Res.
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  4. 2022
    Learn the Time to Learn: Replay Scheduling in Continual LearningMarcus Klasson, Hedvig Kjellström, Cheng ZhangTrans. Mach. Learn. Res.
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  5. 2022
    Centroids Matching: an efficient Continual Learning approach operating in the embedding spaceJary Pomponi, Simone Scardapane, Aurelio UnciniTrans. Mach. Learn. Res.
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  6. 2022PDF ↗
  7. 2022
    Queried Unlabeled Data Improves and Robustifies Class-Incremental LearningTianlong Chen, Sijia Liu, Shiyu Chang … Wang, ZhangyangTrans. Mach. Learn. Res.
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  8. 2022
    Memory-efficient Reinforcement Learning with Value-based Knowledge ConsolidationQingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam MahmoodTrans. Mach. Learn. Res.
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  9. 2022
    Mitigating Catastrophic Forgetting in Spiking Neural Networks through Threshold ModulationIlyass Hammouamri, T. Masquelier, Dennis G. WilsonTrans. Mach. Learn. Res.
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.