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.

12 papers of 8,653Sort Recent · Most cited
  1. 2026
    Towards Experience Replay for Class-Incremental Learning in Fully-Binary NetworksYanis Basso-Bert, Anca Molnos, Romain Lemaire … Antoine DupretACM Transactions · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · CEA Grenoble · +3
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  2. 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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  3. 2025
    Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series ForecastingNouha Karaouli, Denis Coquenet, Élisa Fromont … Marina ReybozarXiv · Institut de Recherche en Informatique et Systèmes Aléatoires · Université de Rennes · +7
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  4. 2025
    Exploring continual learning strategies in artificial neural networks through graph-based analysis of connectivity: Insights from a brain-inspired perspectiveLucrezia Carboni, Dwight Nwaigwe, Marion Mainsant … Sophie AchardNeural Networks · Centre National de la Recherche Scientifique · Inserm · +10
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  5. 2024
    Forgetting Analysis by Module Probing for Online Object Detection with Faster R-CNNBaptiste Wagner, Denis Pellerin, Sylvain HuetEuropean Signal Processing Conference (EUSIPCO) · Université Grenoble Alpes
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  6. 2024
    On Class-Incremental Learning for Fully Binarized Convolutional Neural NetworksYanis Basso-Bert, William Guicquéro, Anca Molnos … Antoine DupretIEEE International Symposium on Circuits and Systems (ISCAS) · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Électronique des Technologies de l'Information · +2
  7. 2023
    On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision TransformersThomas De Min, Massimiliano Mancini, Karteek Alahari … Elisa RicciICCV · University of Trento · Institut polytechnique de Grenoble · +5
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  8. 2022
    Unseen Classes at a Later Time? No ProblemHari Chandana Kuchibhotla, Sumitra S Malagi, Shivam Chandhok, Vineeth N BalasubramanianCVPR · Indian Institute of Technology Hyderabad · Institut national de recherche en sciences et technologies du numérique · +1
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  9. 2018
    End-to-End Incremental LearningFrancisco M. Castro, Manuel J. Marín‐Jiménez, Nicolás Guil … Karteek AlahariECCV · Universidad de Málaga · University of Córdoba · +5
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  10. 2017
    Incremental Learning of Object Detectors without Catastrophic ForgettingKonstantin Shmelkov, Cordelia Schmid, Karteek AlahariICCV · Institut polytechnique de Grenoble · Centre National de la Recherche Scientifique · +3
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  11. 2013
    The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effectsMartial Mermillod, Aurélia Bugaïska, Patrick BoninFrontiers · Centre National de la Recherche Scientifique · Institut Universitaire de France · +3
  12. 2004
    Self-refreshing memory in artificial neural networks: learning temporal sequences without catastrophic forgettingBernard Ans, Stéphane Rousset, Robert M. French, Serban C. MuscaConnection Science · Université Pierre Mendès France · Centre National de la Recherche Scientifique · +3
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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.