Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

12 papers of 11,817Sort Recent · Most cited
  1. 2022
    A study of the Dream Net model robustness across continual learning scenariosMarion Mainsant, Martial Mermillod, Christelle Godin, Marina ReybozICDM · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · CEA Grenoble · +3
  2. 2022
    On the Beneficial Effects of Reinjections for Continual LearningM. Solinas, Marina Reyboz, Stéphane Rousset … Martial MermillodSN Computer Science · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · CEA Grenoble · +3
  3. 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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  4. 2021
    Impact of reverberation through deep neural networks on adversarial perturbationsRomain Cohendet, M. Solinas, Rémi Bernhard … Martial MermillodICML · Laboratoire d'Intégration des Systèmes et des Technologies · Université Grenoble Alpes
  5. 2021
    IncrAMLSI: Incremental Learning of Accurate Planning Domains from Partial and Noisy ObservationsMaxence Grand, Humbert Fiorino, Damien PellierIEEE 33rd International Conference on Tools with Artifici… · Laboratoire d'Informatique de Grenoble · Université Grenoble Alpes
  6. 2021
    Beneficial Effect of Combined Replay for Continual LearningM. Solinas, Stéphane Rousset, Romain Cohendet … Martial MermillodInternational Conference on Agents and Artificial Intelli… · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · CEA Grenoble · +7
  7. 2019
    Continual learning for image classification. (Apprentissage continu pour la classification des images)Anuvabh DuttUniversité Grenoble Alpes (ComUE) · Université Grenoble Alpes
  8. 2019
    Preventing Catastrophic Interference in Multiple-Sequence Learning Using Coupled Reverberating Elman NetworksBernard Ans, Stéphane Rousset, Roheit M. French, Serban C. MuscaCognitive Science · Centre National de la Recherche Scientifique · Laboratoire de Psychologie et NeuroCognition · +2
  9. 2018
    End-to-End Incremental LearningFrancisco M. Castro, Manuel J. Marín‐Jiménez, Nicolás Guil … Karteek AlahariSpringer LNCS · 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
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 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.