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.

240 papers of 8,653 · showing 201–240Sort Recent · Most cited
  1. 2021
    Linear Mode Connectivity in Multitask and Continual LearningSeyed Iman Mirzadeh, Mehrdad Farajtabar, Dilan Görür … Hassan GhasemzadehICLR · Washington State University · Google (United States)
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  2. 2021
    Remembering for the Right Reasons: Explanations Reduce Catastrophic ForgettingSayna Ebrahimi, S. Petryk, Akash Gokul … Trevor DarrellICLR
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  3. 2021
    Lifelong Learning of Compositional StructuresJorge A. Mendez, Eric EatonICLR · California University of Pennsylvania · University of Pennsylvania
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  4. 2021
    Anatomy of Catastrophic Forgetting: Hidden Representations and Task SemanticsVinay Ramasesh, Ethan Dyer, Maithra RaghuICLR · Google (United States) · Cornell University
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  5. 2021
    Graph-Based Continual LearningBinh Tang, David S. MattesonICLR · Cornell University
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  6. 2021
    Wandering within a World: Online Contextualized Few-Shot LearningMengye Ren, Michael L. Iuzzolino, Michael C. Mozer, Richard S. ZemelICLR · University of Toronto · University of Colorado Boulder · +1
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  7. 2021
    Continual learning in recurrent neural networksBenjamin Ehret, Christian Henning, Maria R. Cervera … B. GreweICLR
  8. 2021
    CPR: Classifier-Projection Regularization for Continual LearningSungmin Cha, Hsiang Hsu, Taebaek Hwang … Taesup MoonICLR
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  9. 2020
    Continual Learning with Bayesian Neural Networks for Non-Stationary DataRichard Kurle, Botond Cseke, Alexej Klushyn … Stephan GünnemannICLR
  10. 2020
  11. 2019PDF ↗
  12. 2020
    BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong LearningYeming Wen, Dustin Tran, Jimmy BaICLR · University of Toronto · Google (United States)
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  13. 2020
    A Neural Dirichlet Process Mixture Model for Task-Free Continual LearningSoochan Lee, Junsoo Ha, Dongsu Zhang, Gunhee KimICLR
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  14. 2020
  15. 2020
    Progressive Memory Banks for Incremental Domain AdaptationNabiha Asghar, Lili Mou, Kira A. Selby … Xin JiangICLR
  16. 2020
    Continual Learning with Adaptive Weights (CLAW)Tameem Adel, Han Zhao, Richard E. TurnerICLR
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  17. 2020
    Compositional Language Continual LearningYuanpeng Li, Liang Zhao, Kenneth Ward Church, Mohamed ElhoseinyICLR
  18. 2020
    LAMOL: LAnguage MOdeling for Lifelong Language LearningFan-Keng Sun, Cheng-Hao Ho, Hung-yi LeeICLR · Massachusetts Institute of Technology · National Taiwan University
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  19. 2020
    Uncertainty-guided Continual Learning with Bayesian Neural NetworksSayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus RohrbachICLR · University of California, Berkeley · King Abdullah University of Science and Technology · +1
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  20. 2020
    Continual learning with hypernetworksJohannes von Oswald, Christian Henning, J. Sacramento, B. GreweICLR
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  21. 2019
    A comprehensive, application-oriented study of catastrophic forgetting in DNNsBenedikt Pfülb, Alexander GepperthICLR · Fulda University of Applied Sciences
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  22. 2020
    Scalable and Order-robust Continual Learning with Additive Parameter DecompositionJaehong Yoon, Saehoon Kim, Eunho Yang, Sung Ju HwangICLR · Korea Advanced Institute of Science and Technology
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  23. 2019
    Functional Regularisation for Continual Learning using Gaussian ProcessesMichalis K. Titsias, Jonathan Schwarz, Alexander Matthews … Yee Whye TehICLR · Google (United States)
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  24. 2018
    Variational Continual LearningTurner, RE, Thang D. Bui, Yingzhen Li, Cuong, NguyenICLR · University of Cambridge
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  25. 2019
  26. 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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  27. 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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  28. 2019
    Efficient Lifelong Learning with A-GEMArslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, Mohamed ElhoseinyICLR
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  29. 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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  30. 2019
  31. 2019
    Selfless Sequential LearningRahaf Aljundi, Marcus Rohrbach, Tinne TuytelaarsICLR
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  32. 2019
    Measuring and regularizing networks in function spaceAri S. Benjamin, David Rolnick, Konrad P. KördingICLR · University of Pennsylvania · Philadelphia University
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  33. 2018
    Memory-based Parameter AdaptationPablo Sprechmann, Siddhant M. Jayakumar, Jack W. Rae … Charles BlundellICLR · Google (United States)
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  34. 2018
    Bayesian Incremental Learning for Deep Neural NetworksMax Kochurov, Timur Garipov, Dmitry Podoprikhin … Dmitry VetrovICLR · Skolkovo Institute of Science and Technology · Samsung (South Korea) · +1
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  35. 2018
  36. 2018
    FearNet: Brain-Inspired Model for Incremental LearningRonald Kemker, Christopher KananICLR
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  37. 2018
    Modular Continual Learning in a Unified Visual EnvironmentKevin Feigelis, Blue Sheffer, Daniel YaminsICLR · Rutgers, The State University of New Jersey · Stanford University
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  38. 2018
    Lifelong Learning with Dynamically Expandable NetworksJaehong Yoon, Eunho Yang, Jeongtae Lee, Sung Ju HwangICLR · Korea Advanced Institute of Science and Technology
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  39. 2016
    Net2Net: Accelerating Learning via Knowledge TransferTianqi Chen, Ian Goodfellow, Jonathon ShlensICLR · University of Washington · Google (United States)
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  40. 2014
    An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural NetworksIan Goodfellow, Mehdi Mirza, Xiao Da … Yoshua BengioICLR · Département d'Informatique · Université de Montréal · +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.