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.

18 papers of 8,653Sort Recent · Most cited
  1. 2025
    Bayesian continual learning and forgetting in neural networksDjohan Bonnet, Kellian Cottart, Tifenn Hirtzlin … Damien QuerliozNature Communications · Centre National de la Recherche Scientifique · Université Paris-Saclay · +4
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  2. 2025
    Autonomous retrieval for continuous learning in associative memory networksPaul Saighi, M. J. RozenbergFrontiers · Centre National de la Recherche Scientifique · Université Paris-Saclay · +4
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  3. 2025
    A Reality Check on Pre-training for Exemplar-free Class-Incremental LearningEva Feillet, Adrian Popescu, Céline HudelotWACV · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Université Paris-Saclay · +2
  4. 2024
  5. 2021
  6. 2021
  7. 2021
    Memory Efficient Invertible Neural Networks for Class-Incremental LearningGuillaume Hocquet, Olivier Bichler, Damien QuerliozIEEE 3rd International Conference on Artificial Intellige… · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Intégration des Systèmes et des Technologies · +2
  8. 2021
    Avalanche: an End-to-End Library for Continual LearningVincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu … Davide MaltoniCVPR · University of Pisa · University of Bologna · +12
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  9. 2021
    Synaptic metaplasticity in binarized neural networksAxel Laborieux, Maxence Ernoult, Tifenn Hirtzlin, Damien QuerliozNature Communications · Centre National de la Recherche Scientifique · Université Paris-Saclay · +2
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  10. 2021
    A comparative study of calibration methods for imbalanced class incremental learningUmang Aggarwal, Adrian Popescu, Eden Belouadah, Céline HudelotMultimedia Tools and Applications · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Université Paris-Saclay · +5
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  11. 2021
    Semi-Supervised Class Incremental LearningAlexis Lechat, Stéphane Herbin, Frédéric JurieICPR · Centre National de la Recherche Scientifique · École Nationale Supérieure d'Ingénieurs de Caen · +5
  12. 2021
    Pseudo-Labeling for Class Incremental LearningAlexis Lechat, Stéphane Herbin, Frédéric JurieBMVC · Université Paris-Saclay · Office National d'Études et de Recherches Aérospatiales · +3
  13. 2021
    A Comprehensive Study of Class Incremental Learning Algorithms for Visual TasksEden Belouadah, Adrian Popescu, Ioannis KanellosNeural Networks · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Université Paris-Saclay · +2
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  14. 2020
    OvA-INN: Continual Learning with Invertible Neural NetworksGuillaume Hocquet, Olivier Bichler, Damien QuerliozIJCNN · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Intégration des Systèmes et des Technologies · +2
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  15. 2020
    ScaIL: Classifier Weights Scaling for Class Incremental LearningEden Belouadah, Adrian PopescuWACV · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Université Paris-Saclay · +1
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  16. 2019
    AutoML @ NeurIPS 2018 challenge: Design and ResultsHugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon … Qiang YangMachine Learning · Gleason (United States) · National Institute of Astrophysics, Optics and Electronics · +8
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  17. 2017
    Incremental learning with the minimum description length principlePierre-Alexandre Murena, Antoine Cornuéjols, Jean-Louis DessallesIJCNN · Télécom Paris · Université Paris-Saclay
  18. 2016
    Incremental learning for bootstrapping object classifier modelsCem Karaoguz, Alexander GepperthIEEE Conference Proceedings · Institut national de recherche en sciences et technologies du numérique · École Nationale Supérieure de Techniques Avancées · +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.