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.

7 papers of 8,653Sort Recent · Most cited
  1. 2023
    Building a Subspace of Policies for Scalable Continual LearningJean-Baptiste Gaya, Thang Doan, Lucas Caccia … Roberta RăileanuICLR · Sorbonne Université · Institut Systèmes Intelligents et de Robotique · +2
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  2. 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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  3. 2021
    IIRC: Incremental Implicitly-Refined ClassificationMohamed Abdelsalam, Mojtaba Faramarzi, Shagun Sodhani, Sarath ChandarCVPR · Mila - Quebec Artificial Intelligence Institute · Université de Montréal · +3
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  4. 2021
    Continuum: Simple Management of Complex Continual Learning ScenariosArthur Douillard, Timothée LesortarXiv · Mila - Quebec Artificial Intelligence Institute
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  5. 2020
    More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental LearningYu Liu, Sarah Parisot, Greg Slabaugh … Tinne TuytelaarsECCV · KU Leuven · Huawei Technologies (China) · +1
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  6. 2019
    Continual Learning of New Sound Classes Using Generative ReplayZhepei Wang, Cem Subakan, Efthymios Tzinis … Laurent CharlinIEEE Workshop on Applications of Signal Processing to Aud… · University of Illinois Urbana-Champaign · Mila - Quebec Artificial Intelligence Institute
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  7. 2018
    Transfer Incremental Learning using Data AugmentationGhouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia … Michel JézéquelApplied Sciences · Université de Bretagne Occidentale · Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance · +2
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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.