Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

20 papers of 6,984Sort Recent · Most cited
  1. 2026
    A practical guide to streaming continual learningAndrea Cossu, Federico Giannini, Giacomo Ziffer … Davide BacciuNeurocomputing · University of Pisa · Istituto Nazionale di Fisica Nucleare, Sezione di Pisa · +6
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  2. 2025
    Don't drift away: Advances and Applications of Streaming and Continual LearningAndrea Cossu, Davide Bacciu, Alessio Bernardo … Giacomo ZifferESANN 2025 proceesdings · University of Pisa · Politecnico di Milano · +3
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  3. 2024
    Adiabatic replay for continual learningAlexander Krawczyk, Alexander GepperthIJCNN · Fulda University of Applied Sciences
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  4. 2024
    An analysis of best-practice strategies for replay and rehearsal in continual learningAlexander Krawczyk, Alexander GepperthCVPR · Fulda University of Applied Sciences
  5. 2024
    Continual Learning of Deep Neural Networks in The Age of Big DataAlexander Gepperth, Timothée LesortESANN 2024 proceesdings · Fulda University of Applied Sciences · Altran (France)
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  6. 2024
    Continual Learning: Applications and the Road ForwardEli Verwimp, Rahaf Aljundi, Shai Ben-David … Gido M. van de VenTMLR
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  7. 2022
    Beyond Supervised Continual Learning: a ReviewBenedikt Bagus, Alexander Gepperth, Timothée LesortarXiv
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  8. 2022
    A Study of Continual Learning Methods for Q-LearningBenedikt Bagus, Alexander GepperthIJCNN · Fulda University of Applied Sciences
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  9. 2022
    An empirical comparison of generators in replay-based continual learningNADZEYA DZEMIDOVICH, Alexander GepperthESANN 2022 proceedings · Fulda University of Applied Sciences
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  10. 2022
    Tutorial - Continual Learning beyond classificationAlexander Gepperth, Timothée LesortESANN 2022 proceedings · Fulda University of Applied Sciences · Université de Montréal
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  11. 2021
    An Investigation of Replay-based Approaches for Continual LearningBenedikt Bagus, Alexander GepperthIEEE International Joint Conference on Neural Network · Fulda University of Applied Sciences
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  12. 2021
    Overcoming Catastrophic Forgetting with Gaussian Mixture ReplayBenedikt Pfülb, Alexander GepperthIEEE International Joint Conference on Neural Network · Fulda University of Applied Sciences
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  13. 2021
    Continual Learning with Fully Probabilistic ModelsBenedikt Pfülb, Alexander Gepperth, Benedikt BagusarXiv · Fulda University of Applied Sciences
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  14. 2019
    A comprehensive, application-oriented study of catastrophic forgetting in DNNsBenedikt Pfülb, Alexander GepperthICLR · Fulda University of Applied Sciences
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  15. 2019
    Incremental learning with a homeostatic self-organizing neural modelAlexander GepperthNeural Computing and Applications · Fulda University of Applied Sciences
  16. 2019
    Marginal Replay vs Conditional Replay for Continual LearningTimothée Lesort, Alexander Gepperth, Andrei Stoian, David FilliatSpringer LNCS · École Nationale Supérieure de Techniques Avancées · Thales (France) · +1
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  17. 2019
    A Study on Catastrophic Forgetting in Deep LSTM NetworksMonika Schak, Alexander GepperthSpringer LNCS · Fulda University of Applied Sciences
  18. 2019
    Simplified Computation and Interpretation of Fisher Matrices in Incremental Learning with Deep Neural NetworksAlexander Gepperth, Florian WiechSpringer LNCS · Fulda University of Applied Sciences
  19. 2018
    Catastrophic Forgetting: Still a Problem for DNNsBenedikt Pfülb, Alexander Gepperth, Syahrul Afzal Che Abdullah, Axel KilianSpringer LNCS · Fulda University of Applied Sciences
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  20. 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. It lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.