Related work

The foundational work on continual learning, 1991 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

4,574 papers · showing 4351–4400Sort Recent · Most cited
  1. 2019
    Task-Free Continual LearningRahaf Aljundi, Klaas Kelchtermans, Tinne TuytelaarsCVPR · KU Leuven
    PDF ↗
  2. 2019
    Learning to Remember: A Synaptic Plasticity Driven Framework for Continual LearningOleksiy Ostapenko, Mihai Puscas, Tassilo Klein … Moin NabiCVPR · Humboldt-Universität zu Berlin · University of Trento · +1
    PDF ↗
  3. 2019
    Meta-Learning Representations for Continual LearningKhurram Javed, Martha WhiteNeurIPS · University of Alberta
    PDF ↗
  4. 2019
    Leveraging Semantics for Incremental Learning in Multi-Relational EmbeddingsAngel Daruna, Weiyu Liu, Zsolt Kira, Sonia ChernovaarXiv · Georgia Institute of Technology
    PDF ↗
  5. 2019
    Uncertainty-based Continual Learning with Adaptive RegularizationHongjoon Ahn, Sungmin Cha, Dong-Gyu Lee, Taesup MoonNeurIPS · Sungkyunkwan University
    PDF ↗
  6. 2019PDF ↗
  7. 2019
    Variational Prototype Replays for Continual LearningMengmi Zhang, Tao Wang, Joo‐Hwee Lim … Jiashi FengarXiv · Harvard University Press
    PDF ↗
  8. 2019
    A comprehensive, application-oriented study of catastrophic forgetting in DNNsBenedikt Pfülb, Alexander GepperthICLR · Fulda University of Applied Sciences
    PDF ↗
  9. 2019
    Continual Learning in Deep Neural Network by Using a Kalman OptimiserHonglin Li, Shirin Enshaeifar, Frieder Ganz, Payam BarnaghiarXiv
    PDF ↗
  10. 2019
    Label Mapping Neural Networks with Response Consolidation for Class Incremental LearningXu Zhang, Yao Yang, Baile Xu … Qingwei LinarXiv · Nanjing University · Microsoft Research (United Kingdom)
    PDF ↗
  11. 2019
    Resource-aware Elastic Swap Random Forest for Evolving Data StreamsDiego Marrón, Eduard Ayguadé, José R. Herrero, Albert BifetarXiv · Barcelona Supercomputing Center · Universitat Politècnica de Catalunya · +1
    PDF ↗
  12. 2019
    Hierarchically Structured Meta-learningHuaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui LiICML · Pennsylvania State University
    PDF ↗
  13. 2019
    Locally Weighted Regression Pseudo-Rehearsal for Online Learning of Vehicle DynamicsGrady Williams, Brian Goldfain, James M. Rehg, Evangelos A. TheodorouarXiv
    PDF ↗
  14. 2019
    Bayesian Optimized Continual Learning with Attention MechanismJu Xu, Jin Ma, Zhanxing ZhuarXiv · Peking University
    PDF ↗
  15. 2019
    Model Primitive Hierarchical Lifelong Reinforcement LearningBohan Wu, Jayesh K. Gupta, Mykel J. KochenderferInternational Joint Conference on Autonomous Agents and M… · Columbia University · Stanford University
    PDF ↗
  16. 2019PDF ↗
  17. 2019
    Improving and Understanding Variational Continual LearningSiddharth Swaroop, Cuong V. Nguyen, Thang D. Bui, Richard E. TurnerNeurIPS
    PDF ↗
  18. 2019
    Memory Efficient Experience Replay for Streaming LearningTyler L. Hayes, Nathan D. Cahill, Christopher KananICRA · Rochester Institute of Technology
    PDF ↗
  19. 2019
    Facilitating Bayesian Continual Learning by Natural Gradients and Stein GradientsYu Chen, Tom Diethe, Neil D. LawrencearXiv · University of Bristol · Amazon (Germany)
    PDF ↗
  20. 2019
    Continual Learning with Self-Organizing MapsPouya Bashivan, Martin Schrimpf, Robert Ajemian … Yuhai TuarXiv · Massachusetts Institute of Technology
    PDF ↗
  21. 2019
    Unsupervised Learning to Overcome Catastrophic Forgetting in Neural NetworksIrene Muñoz-Martín, S. Bianchi, Giacomo Pedretti … Daniele IelminiIEEE Journal on Exploratory Solid-State Computational Dev… · IBM Research - Almaden
    PDF ↗
  22. 2019
    A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech RecognitionJiabin Xue, Jiqing Han, Tieran Zheng … Jiaxing GuoarXiv · Harbin Institute of Technology
    PDF ↗
  23. 2019
    Three scenarios for continual learningGido M. van de Ven, Andreas S. ToliasNeurIPS
    PDF ↗
  24. 2019
    Continuous Learning in Single-Incremental-Task ScenariosDavide Maltoni, Vincenzo LomonacoNeural Networks · University of Bologna
    PDF ↗
  25. 2019PDF ↗
  26. 2019
    M2KD: Multi-model and Multi-level Knowledge Distillation for Incremental LearningPeng Zhou, Long Mai, Jianming Zhang … Larry S. DavisarXiv
    PDF ↗
  27. 2019PDF ↗
  28. 2019
    Efficient Incremental Learning for Mobile Object DetectionDawei Li, Şerafettin Taşcı, Shalini Ghosh … Larry HeckarXiv · Samsung (United States) · Research!America (United States) · +3
    PDF ↗
  29. 2019
    Signal Conditioning for Learning in the WildAyon Borthakur, Thomas A. ClelandNeuro Inspired Computational Elements Workshop · Cornell University
    PDF ↗
  30. 2019
    Gradient based sample selection for online continual learningRahaf Aljundi, Min Lin, Baptiste Goujaud, Yoshua BengioNeurIPS · KU Leuven · National University of Singapore · +1
    PDF ↗
  31. 2019PDF ↗
  32. 2019
    Continual Learning in PracticeTom Diethe, Tom Borchert, Eno Thereska … Neil D. LawrenceNeurIPS · Amazon (Germany)
    PDF ↗
  33. 2019
    Continual Learning via Neural PruningSiavash Golkar, M. Kagan, Kyunghyun ChoarXiv
    PDF ↗
  34. 2019
    Using World Models for Pseudo-Rehearsal in Continual LearningNicholas Ketz, Soheil Kolouri, Praveen K. PillyarXiv · HRL Laboratories (United States)
    PDF ↗
  35. 2019
    Attention-Based Structural-PlasticitySoheil Kolouri, Nicholas Ketz, Xinyun Zou … Praveen K. PillyarXiv · HRL Laboratories (United States)
    PDF ↗
  36. 2019
    Continual Learning with Tiny Episodic MemoriesArslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny … Marc’Aurelio RanzatoarXiv · University of Oxford
    PDF ↗
  37. 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
    PDF ↗
  38. 2019
    Online Meta-LearningChelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey LevineICML · Stanford University · University of Washington · +3
    PDF ↗
  39. 2019
    Generative Memory for Lifelong Reinforcement LearningAswin Raghavan, Jesse Hostetler, Sek ChaiarXiv
    PDF ↗
  40. 2019
    Adaptive Masked Weight Imprinting for Few-Shot SegmentationMennatullah Siam, Boris N. Oreshkin, Martin JägersandarXiv · University of Alberta
    PDF ↗
  41. 2019
    A Unifying Bayesian View of Continual LearningSebastian Farquhar, Yarin GalNeurIPS · University of Oxford
    PDF ↗
  42. 2019
    Differentially Private Continual LearningSebastian Farquhar, Yarin GalarXiv · University of Oxford
    PDF ↗
  43. 2019
    Superposition of Many Models into OneBrian Cheung, A. L. Terekhov, Yubei Chen … Bruno A. OlshausenNeurIPS · University of California, Berkeley
    PDF ↗
  44. 2019
    EILearn: Learning Incrementally Using Previous Knowledge Obtained From an Ensemble of ClassifiersShivang Agarwal, C. Ravindranath Chowdary, Shripriya MaheshwariarXiv
    PDF ↗
  45. 2019PDF ↗
  46. 2019
    Policy Consolidation for Continual Reinforcement LearningChristos Kaplanis, Murray Shanahan, Claudia ClopathICML · Imperial College London
    PDF ↗
  47. 2019
    Learning and Evaluating General Linguistic IntelligenceDani Yogatama, Cyprien de Masson d’Autume, Jerome T. Connor … Phil BlunsomarXiv · Google (United States)
    PDF ↗
  48. 2019
    Functional Regularisation for Continual Learning using Gaussian ProcessesMichalis K. Titsias, Jonathan Schwarz, Alexander Matthews … Yee Whye TehICLR · Google (United States)
    PDF ↗
  49. 2019PDF ↗
  50. 2018
    Variational Continual LearningTurner, RE, Thang D. Bui, Yingzhen Li, Cuong, NguyenICLR · University of Cambridge
    PDF ↗
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, and only papers with a PDF we can point you at, so every title opens the paper itself. 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.