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.

10 papers of 11,817Sort Recent · Most cited
  1. 2025
    Addressing The Devastating Effects Of Single-Task Data Poisoning In Exemplar-Free Continual LearningS. Pawlak, Bartłomiej Twardowski, Tomasz Trzcinski, Joost van de WeijerarXiv
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  2. 2024
    Exploring the Stability Gap in Continual Learning: The Role of the Classification HeadWojciech Łapacz, Daniel Marczak, Filip Szatkowski, Tomasz TrzcinskiWACV
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  3. 2024
    Task-recency bias strikes back: Adapting covariances in Exemplar-Free Class Incremental LearningSebastian Cygert, Grzegorz Rype's'c, Tomasz Trzcinski, Bartłomiej TwardowskiNeurIPS
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  4. 2024
    MagMax: Leveraging Model Merging for Seamless Continual LearningDaniel Marczak, Bartłomiej Twardowski, Tomasz Trzcinski, Sebastian CygertECCV
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  5. 2024
    Improving Continual Learning Performance and Efficiency with Auxiliary ClassifiersFilip Szatkowski, Yaoyue Zheng, Fei Yang … Joost van de WeijerICML
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  6. 2024
    GUIDE: Guidance-based Incremental Learning with Diffusion ModelsBartosz Cywi'nski, Kamil Deja, Tomasz Trzcinski … Lukasz Kuci'nskiEuropean Conference on Artificial Intelligence
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  7. 2024
    Divide and not forget: Ensemble of selectively trained experts in Continual LearningGrzegorz Rype's'c, Sebastian Cygert, Valeriya Khan … Bartłomiej TwardowskiICLR
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  8. 2023
    Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from GeneralizationSebastian Dziadzio, cCaugatay Yildiz, Gido M. van de Ven … Matthias BethgeCoLLAs
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  9. 2023
    Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual LearningFilip Szatkowski, Mateusz Pyla, Marcin Przewiȩźlikowski … Tomasz TrzcinskiICCV
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  10. 2023
    Exploring Continual Learning of Diffusion ModelsMichal Zajac, K. Deja, Anna Kuzina … Piotr Milo'sarXiv
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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.