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.

7 papers of 11,817Sort Recent · Most cited
  1. 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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  2. 2023
    Continual Learning of Diffusion Models with Generative DistillationSergi Masip Cabeza, Pau Rodríguez, T. Tuytelaars, Gido M. van de VenCoLLAs
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  3. 2023
    Continual Learning: Applications and the Road ForwardEli Verwimp, S. Ben-David, Matthias Bethge … Gido M. van de VenTrans. Mach. Learn. Res.
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  4. 2023PDF ↗
  5. 2023
    Prediction Error-based Classification for Class-Incremental LearningMichal Zajkac, T. Tuytelaars, Gido M. van de VenICLR
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  6. 2023
    Knowledge Accumulation in Continually Learned Representations and the Issue of Feature ForgettingTimm Hess, Eli Verwimp, Gido M. van de Ven, T. TuytelaarsTrans. Mach. Learn. Res.
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  7. 2023
    Deep Continual Learning (Dagstuhl Seminar 23122)T. Tuytelaars, Bing Liu, Vincenzo Lomonaco … Andrea CossuDagstuhl Reports
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.