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.

167 papers of 4,574 · showing 51–100Sort Recent · Most cited
  1. 2019
    Continual learning: A comparative study on how to defy forgetting in classification tasksMatthias De Lange, Rahaf Aljundi, Marc Masana … T. TuytelaarsTPAMI
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  2. 2019
    Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong LearningXin Yao, Tianchi Huang, Chenglei Wu … Lifeng SunNeural Computation · Tsinghua University
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  3. 2019
    Neural Architecture Search for Class-incremental LearningShenyang Huang, Vincent François-Lavet, Guillaume RabusseauarXiv
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  4. 2019PDF ↗
  5. 2019
    Learning sparse representations in reinforcement learningJacob Rafati, David C. NoellearXiv · University of California, Merced
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  6. 2019
    Learning Continually from Low-shot Data StreamCanyu Le, Xihan Wei, Biao Wang … Chen, ZhongguiarXiv
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  7. 2019
    Collaborative Method for Incremental Learning on Classification and GenerationByungju Kim, Jae-Young Lee, Kyungsu Kim … Junmo KimICIP · Korea Advanced Institute of Science and Technology · Samsung (United States)
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  8. 2019
    Online Continual Learning with Maximally Interfered RetrievalRahaf Aljundi, Lucas Caccia, Eugene Belilovsky … Tinne TuytelaarsarXiv · McGill University · Université de Montréal · +1
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  9. 2019
    Continual learning of context-dependent processing in neural networksGuanxiong Zeng, Yang Chen, Bo Cui, Shan YuNature Machine Intelligence · Chinese Academy of Sciences · Institute of Automation · +2
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  10. 2019
    Visualizing the PHATE of Neural NetworksScott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal MishneNeurIPS · Yale University · Princeton University · +1
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  11. 2019
    Toward Understanding Catastrophic Forgetting in Continual LearningCuong V. Nguyen, Alessandro Achille, Michael Lam … Stefano SoattoarXiv
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  12. 2019PDF ↗
  13. 2019
    Biologically inspired sleep algorithm for artificial neural networksGiri P. Krishnan, Timothy Tadros, Ramyaa Ramyaa, Maxim BazhenovarXiv
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  14. 2019
    DynMat, a network that can learn after learningJung H. LeePubMed · Allen Institute for Brain Science · Allen Institute
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  15. 2019PDF ↗
  16. 2019
    Complementary Learning for Overcoming Catastrophic Forgetting Using Experience ReplayMohammad Rostami, Soheil Kolouri, Praveen K. PillyIJCAI · California University of Pennsylvania · University of Pennsylvania · +1
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  17. 2019
    Closed-Loop Memory GAN for Continual LearningAmanda Rios, Laurent IttiIJCAI · University of Southern California · California Southern University
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  18. 2019
    Learning Shared Knowledge for Deep Lifelong Learning using Deconvolutional NetworksSeungwon Lee, James Stokes, Eric EatonIJCAI · University of Pennsylvania · Flatiron Health (United States) · +1
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  19. 2019
    Extensible Cross-Modal HashingTianyi Chen, Lan Zhang, Shi-cong Zhang … Bai-chuan HuangIJCAI · University of Science and Technology of China · Northeastern University · +1
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  20. 2019
    Self-Organizing Incremental Neural Networks for Continual LearningChayut Wiwatcharakoses, Daniel BerrarIJCAI · Tokyo Institute of Technology
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  21. 2019
    Towards AutoML in the presence of Drift: first resultsJorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales … Michèle SébagIJCAI · National Institute of Astrophysics, Optics and Electronics · Nanjing University · +2
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  22. 2019
    Adaptive Compression-based Lifelong LearningShivangi Srivastava, Maxim Berman, Matthew B. Blaschko, Devis TuiaSocio-Environmental Systems Modeling · Wageningen University & Research · KU Leuven
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  23. 2019
    Application of a Selective Desensitization Neural Network to Concept Drift ProblemsIchiba Tomoki, Kazumasa Horie, Someno Shoichi … Masahiko MoritaJournal of Signal Processing · University of Tsukuba
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  24. 2019
    Scalable Recollections for Continual Lifelong LearningMatthew Riemer, Tim Klinger, Djallel Bouneffouf, Michele FranceschiniAAAI
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  25. 2019
    The Utility of Sparse Representations for Control in Reinforcement LearningVincent Liu, Raksha Kumaraswamy, Lei Le, Martha WhiteAAAI · University of Alberta · Indiana University Bloomington
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  26. 2019
    Rethinking Continual Learning for Autonomous Agents and RobotsGerman I. Parisi, Christopher KananarXiv · Universität Hamburg · Rochester Institute of Technology
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  27. 2019
    Generative Models from the perspective of Continual LearningTimothée Lesort, Hugo Caselles-Dupré, Michael Garcia-Ortiz … David FilliatIEEE International Joint Conference on Neural Network · Institut national de recherche en sciences et technologies du numérique · École Nationale Supérieure de Techniques Avancées · +2
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  28. 2019
    A Class-Incremental Learning Method Based on One Class Support Vector MachineChengfei Yao, Jie Zou, Yanan Luo … Gang BaiJournal of Physics Conference Series · Nankai University
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  29. 2019
    Interleaved training prevents catastrophic forgetting in spiking neural networksRyan Golden, Jean Erik Delanois, Pavel Šanda, Maxim BazhenovbioRxiv · University of California San Diego · Czech Academy of Sciences · +1
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  30. 2019
    A Spike Time-Dependent Online Learning Algorithm Derived From Biological OlfactionAyon Borthakur, Thomas A. ClelandFrontiers · Cornell University
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  31. 2019
    Beneficial perturbation network for continual learningShixian Wen, Laurent IttiarXiv · University of Southern California · California Southern University
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  32. 2019
    Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-CorrectionFengda Zhu, Xiaojun Chang, Runhao Zeng, Mingkui TanarXiv · South China University of Technology
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  33. 2019PDF ↗
  34. 2019
    Conditional Computation for Continual LearningMin Lin, Jie Fu, Yoshua BengioarXiv
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  35. 2019
    Task Agnostic Continual Learning via Meta LearningXu He, Jakub Sygnowski, Alexandre Galashov … Razvan PascanuarXiv · Google (United States)
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  36. 2019
    Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real TransferRené Traoré, Hugo Caselles-Dupré, Timothée Lesort … David FilliatarXiv
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  37. 2019
    Forward and Backward Knowledge Transfer for Sentiment ClassificationHao Wang, Bing Liu, Shuai Wang … Yan YangMachine Learning · Southwest Jiaotong University · University of Illinois Chicago
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  38. 2019PDF ↗
  39. 2019
    Episodic Memory in Lifelong Language LearningCyprien de Masson d’Autume, Sebastian Ruder, Lingpeng Kong, Dani YogatamaNeurIPS
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  40. 2019
    An Adaptive Random Path Selection Approach for Incremental Learning.Jathushan Rajasegaran, Munawar Hayat, Salman Khan … Ming–Hsuan YangNeurIPS
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  41. 2019
    Large Scale Incremental LearningYue Wu, Yinpeng Chen, Lijuan Wang … Yun FuCVPR · Northeastern University · Universidad del Noreste · +2
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  42. 2019
    Learning Without MemorizingPrithviraj Dhar, Rajat Singh, Kuan–Chuan Peng … Rama ChellappaCVPR · University of Maryland, College Park · Siemens (Germany)
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  43. 2019
    Task-Free Continual LearningRahaf Aljundi, Klaas Kelchtermans, Tinne TuytelaarsCVPR · KU Leuven
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  44. 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
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  45. 2019
    Meta-Learning Representations for Continual LearningKhurram Javed, Martha WhiteNeurIPS · University of Alberta
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  46. 2019
    Leveraging Semantics for Incremental Learning in Multi-Relational EmbeddingsAngel Daruna, Weiyu Liu, Zsolt Kira, Sonia ChernovaarXiv · Georgia Institute of Technology
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  47. 2019
    Uncertainty-based Continual Learning with Adaptive RegularizationHongjoon Ahn, Sungmin Cha, Dong-Gyu Lee, Taesup MoonNeurIPS · Sungkyunkwan University
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  48. 2019PDF ↗
  49. 2019
    Variational Prototype Replays for Continual LearningMengmi Zhang, Tao Wang, Joo‐Hwee Lim … Jiashi FengarXiv · Harvard University Press
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  50. 2019
    A comprehensive, application-oriented study of catastrophic forgetting in DNNsBenedikt Pfülb, Alexander GepperthICLR · Fulda University of Applied Sciences
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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. 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.