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.

11 papers of 8,653Sort Recent · Most cited
  1. 2023
    Towards Compute-Optimal Transfer LearningM. Caccia, Alexandre Galashov, Arthur Douillard … Razvan PascanuarXiv
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  2. 2023
    NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision ResearchJörg Bornschein, Alexandre Galashov, Ross Hemsley … Marc’Aurelio RanzatoJMLR
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  3. 2023
    Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming ChallengesM. Caccia, Jonas Mueller, Taesup Kim … Rasool FakoorCoLLAs
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  4. 2021
    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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  5. 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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  6. 2021
    Learning where to learn: Gradient sparsity in meta and continual learningJohannes von Oswald, Dominic Zhao, Seijin Kobayashi … João SacramentoNeurIPS
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  7. 2021
    Sequoia: A Software Framework to Unify Continual Learning ResearchFabrice Normandin, Florian Golemo, Oleksiy Ostapenko … M. CacciaarXiv
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  8. 2021PDF ↗
  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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  10. 2019
    Online Learned Continual Compression with Stacked Quantization ModuleLucas Caccia, Eugene Belilovsky, M. Caccia, Joëlle PineauarXiv · McGill University · Meta (Israel)
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  11. 2019
    Online Continual Learning with Maximally Interfered RetrievalRahaf Aljundi, Lucas Caccia, Eugene Belilovsky … Tinne TuytelaarsarXiv · McGill University · Université de Montréal · +1
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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.