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.

277 papers of 6,984 · showing 251–277Sort Recent · Most cited
  1. 2019
    Central-Diffused Instance Generation Method in Class Incremental LearningMing-Yu Liu, Yijie WangSpringer LNCS · National University of Defense Technology
  2. 2019
    Transfer Learning with Sparse Associative MemoriesQuentin Jodelet, Vincent Gripon, Masafumi HagiwaraSpringer LNCS · Keio University · IMT Atlantique
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  3. 2019
  4. 2019
    Does an LSTM forget more than a CNN? An empirical study of catastrophic forgetting in NLPGaurav Arora, Afshin Rahimi, Timothy BaldwinAustralasian Language Technology Association Workshop
  5. 2019
    Uncertainty-Guided Continual Learning in Bayesian Neural Networks - Extended AbstractSayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus RohrbachCVPR
  6. 2019
    Differentiable Hebbian Plasticity for Continual LearningVithursan Thangarasa, Thomas Miconi, Graham W. TaylorPreprint
  7. 2019
    Meta-analysis of Continual LearningCuong V Nguyen, A. Achille, Michael Lam … Stefano SoattoPreprint
  8. 2019
  9. 2019
  10. 2019
    Efficient Continual Learning with Latent RehearsalGabriele Graffieti, Lorenzo Pellegrini, V. Lomonaco, D. MaltoniPreprint
  11. 2019
  12. 2019
  13. 2019
  14. 2019
  15. 2019
    Task-Free Continual Learning Supplementary MaterialsRahaf Aljundi, Klaas KelchtermansPreprint
  16. 2019
    Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RLAnusha Nagabandi, Chelsea Finn, Sergey LevineICLR · University of California, Berkeley
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  17. 2019
    Reconciling meta-learning and continual learning with online mixtures of tasksGhassen Jerfel, Erin Grant, T. Griffiths, K. HellerNeurIPS
  18. 2019
    An Empirical Study of Example Forgetting during Deep Neural Network LearningMariya Toneva, Alessandro Sordoni, Rémi Tachet des Combes … Geoffrey J. GordonICLR · Carnegie Mellon University · Microsoft (United States) · +1
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  19. 2019
    Efficient Lifelong Learning with A-GEMArslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, Mohamed ElhoseinyICLR
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  20. 2019
    Experience Replay for Continual LearningDavid Rolnick, Arun Ahuja, Jonathan Schwarz … Greg WayneNeurIPS · California University of Pennsylvania · University of Pennsylvania · +1
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  21. 2019
    Learning to Learn without Forgetting By Maximizing Transfer and Minimizing InterferenceMatthew Riemer, Ignacio Cases, Robert Ajemian … Gerald TesauroICLR · IBM (United States) · Stanford University · +2
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  22. 2019
  23. 2019
    Perception Coordination Network: A Neuro Framework for Multimodal Concept Acquisition and BindingYoulu Xing, Xiaofeng Shi, Furao Shen … Ah‐Hwee TanTNNLS · Anhui University · Nanjing University · +2
  24. 2019
    Selfless Sequential LearningRahaf Aljundi, Marcus Rohrbach, Tinne TuytelaarsICLR
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  25. 2019
    Measuring and regularizing networks in function spaceAri S. Benjamin, David Rolnick, Konrad P. KördingICLR · University of Pennsylvania · Philadelphia University
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  26. 2019
    Continual Lifelong Learning with Neural Networks: A ReviewG. I. Parisi, Ronald Kemker, Jose L. Part … Stefan WermterNeural Networks
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  27. 2019
    A Reinforcement Learning Architecture That Transfers Knowledge Between Skills When Solving Multiple TasksPaolo Tommasino, Daniele Caligiore, Marco Mirolli, Gianluca BaldassarreIEEE TCDS · Nanyang Technological University · Institute of Cognitive Sciences and Technologies
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.