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. 2023
    Continual Learning of Diffusion Models with Generative DistillationSergi Masip Cabeza, Pau Rodríguez, T. Tuytelaars, Gido M. van de VenCoLLAs
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  2. 2022PDF ↗
  3. 2022
    Challenging Common Assumptions about Catastrophic Forgetting and Knowledge AccumulationTimothée Lesort, Оleksiy Ostapenko, Diganta Misra … Irina RishCoLLAs
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  4. 2022
    Continual Learning with Foundation Models: An Empirical Study of Latent ReplayОleksiy Ostapenko, Timothée Lesort, Pau Rodríguez … Laurent CharlinCoLLAs
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  5. 2022
    Overcoming challenges in leveraging GANs for few-shot data augmentationChristopher Beckham, Issam Laradji, Pau Rodríguez … Christopher PalCoLLAs
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  6. 2020
    CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future DirectionsVincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodríguez … Davide MaltoniArtificial Intelligence · University of Bologna · Mila - Quebec Artificial Intelligence Institute · +7
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  7. 2021
    Continual Learning via Local Module CompositionOleksiy Ostapenko, Pau Rodríguez, M. Caccia, Laurent CharlinNeurIPS · Taras Shevchenko National University of Kyiv · University of Insubria · +1
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  8. 2021
    Sequoia: A Software Framework to Unify Continual Learning ResearchFabrice Normandin, Florian Golemo, Oleksiy Ostapenko … M. CacciaarXiv
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  9. 2020
    Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual LearningM. Caccia, Pau Rodríguez, Оleksiy Ostapenko … Laurent CharlinNeurIPS
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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.