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.

319 papers of 8,653 · showing 151–200Sort Recent · Most cited
  1. 2019
    Conditional Computation for Continual LearningMin Lin, Jie Fu, Yoshua BengioarXiv
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  2. 2019
    Predictive EWC: mitigating catastrophic forgetting of neural network through pre-prediction of learning dataDae-Yong Hong, Yan Li, Byeong‐Seok ShinJournal of Ambient Intelligence and Humanized Computing · Inha University
  3. 2019
    Task Agnostic Continual Learning via Meta LearningXu He, Jakub Sygnowski, Alexandre Galashov … Razvan PascanuarXiv · Google (United States)
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  4. 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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  5. 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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  6. 2019PDF ↗
  7. 2019
    Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong LearningSteven C. Y. Hung, Jia‐Hong Lee, Timmy S. T. Wan … Chu‐Song Chen2019 on International Conference on Multimedia Retrieval · Institute of Information Science, Academia Sinica · Research Center for Information Technology Innovation, Academia Sinica
  8. 2019
    Take Goods from Shelves: A Dataset for Class-Incremental Object DetectionHao Yu, Yanwei Fu, Yu–Gang Jiang2019 on International Conference on Multimedia Retrieval · Fudan University · Jilian Technology Group (China)
  9. 2019
    Episodic Memory in Lifelong Language LearningCyprien de Masson d’Autume, Sebastian Ruder, Lingpeng Kong, Dani YogatamaNeurIPS
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  10. 2019
    Random Path Selection for Incremental LearningJathushan Rajasegaran, Munawar Hayat, Salman Hameed Khan … Ling ShaoNeurIPS
  11. 2019
    An Adaptive Random Path Selection Approach for Incremental Learning.Jathushan Rajasegaran, Munawar Hayat, Salman Khan … Ming–Hsuan YangNeurIPS
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  12. 2019
    Large Scale Incremental LearningYue Wu, Yinpeng Chen, Lijuan Wang … Yun FuCVPR · Northeastern University · Universidad del Noreste · +2
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  13. 2019
    Learning a Unified Classifier Incrementally via RebalancingSaihui Hou, Xinyu Pan, Chen Change Loy … Dahua LinCVPR · University of Science and Technology of China · XLAB (Slovenia) · +3
  14. 2019
    Learning Without MemorizingPrithviraj Dhar, Rajat Singh, Kuan–Chuan Peng … Rama ChellappaCVPR · University of Maryland, College Park · Siemens (Germany)
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  15. 2019
    Task-Free Continual LearningRahaf Aljundi, Klaas Kelchtermans, Tinne TuytelaarsCVPR · KU Leuven
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  16. 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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  17. 2019
    Incremental Object Learning From Contiguous ViewsStefan Stojanov, Samarth Mishra, Ngoc Anh Thai … James M. RehgCVPR · Georgia Institute of Technology · Indiana University Bloomington
  18. 2019
    Energy-efficient continual learning in hybrid supervised-unsupervised neural networks with PCM synapsesS. Bianchi, Irene Muñoz-Martín, Giacomo Pedretti … Daniele IelminiSymposium on VLSI Technology · Politecnico di Milano · IBM Research - Almaden
  19. 2019
    Kernel-Based Efficient Lifelong Learning AlgorithmSeung-Jun Kim, Rami MowakeaaIEEE Data Science Workshop (DSW) · University of Maryland, Baltimore County
  20. 2019
  21. 2019
    Meta-Learning Representations for Continual LearningKhurram Javed, Martha WhiteNeurIPS · University of Alberta
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  22. 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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  23. 2019
    Uncertainty-based Continual Learning with Adaptive RegularizationHongjoon Ahn, Sungmin Cha, Dong-Gyu Lee, Taesup MoonNeurIPS · Sungkyunkwan University
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  24. 2019
  25. 2019
    Variational Prototype Replays for Continual LearningMengmi Zhang, Tao Wang, Joo‐Hwee Lim … Jiashi FengarXiv · Harvard University Press
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  26. 2019
    Prototype Reminding for Continual LearningMengmi Zhang, Tao Wang, J. Lim, Jiashi FengarXiv
  27. 2019
    Lifelong learning and inductive biasRon Amit, Ron MeirCurrent Opinion in Behavioral Sciences · Technion – Israel Institute of Technology
  28. 2019
    Lifelong learning in artificial neural networksGary AnthesCommunications of the ACM
  29. 2019
    A comprehensive, application-oriented study of catastrophic forgetting in DNNsBenedikt Pfülb, Alexander GepperthICLR · Fulda University of Applied Sciences
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  30. 2019
    Continual Learning in Deep Neural Network by Using a Kalman OptimiserHonglin Li, Shirin Enshaeifar, Frieder Ganz, Payam BarnaghiarXiv
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  31. 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)
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  32. 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
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  33. 2019
    Hierarchically Structured Meta-learningHuaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui LiICML · Pennsylvania State University
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  34. 2019
    Locally Weighted Regression Pseudo-Rehearsal for Online Learning of Vehicle DynamicsGrady Williams, Brian Goldfain, James M. Rehg, Evangelos A. TheodorouarXiv
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  35. 2019
    Bayesian Optimized Continual Learning with Attention MechanismJu Xu, Jin Ma, Zhanxing ZhuarXiv · Peking University
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  36. 2019
    How to make a machine learn continuously: a tutorial of the Bayesian approachKhoat Than, Xuan Bui, Tung Nguyen-Trong … Anh Nguyen‐DucArtificial Intelligence and Machine Learning for Multi-Do… · Hanoi University of Science and Technology
  37. 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
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  38. 2019
    Building Knowledge for AI Agents with Reinforcement LearningDoina PrecupAdaptive Agents and Multi-Agents Systems · McGill University
  39. 2019PDF ↗
  40. 2019
    Autonomous Deep Learning: Continual Learning Approach for Dynamic EnvironmentsAndri Ashfahani, Mahardhika PratamaSociety for Industrial and Applied Mathematics eBooks
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  41. 2019
    Improving and Understanding Variational Continual LearningSiddharth Swaroop, Cuong V. Nguyen, Thang D. Bui, Richard E. TurnerNeurIPS
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  42. 2019
    Deep stacked stochastic configuration networks for lifelong learning of non-stationary data streamsMahardhika Pratama, Dianhui WangInformation Sciences · Nanyang Technological University · La Trobe University · +1
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  43. 2019
    Memory Efficient Experience Replay for Streaming LearningTyler L. Hayes, Nathan D. Cahill, Christopher KananICRA · Rochester Institute of Technology
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  44. 2019
    Lifelong Learning for Heterogeneous Multi-Modal TasksHuaping Liu, Fuchun Sun, Bin FangICRA · Tsinghua University
  45. 2019
    Incremental Learning of SVM Using Backward Elimination and Forward Selection of Support VectorsVenkata Pesala, Arun Kumar Kalakanti, Topon Kumar Paul … H.G.S. Praneeth BugataInternational Conference on Applied Machine Learning (ICAML) · Toshiba (Japan)
  46. 2019
    Incremental Learning Meets Reduced Precision NetworksYuhuang Hu, Tobi Delbrück, Shih‐Chii LiuIEEE International Symposium on Circuits and Systems (ISCAS) · SIB Swiss Institute of Bioinformatics · University of Zurich · +1
  47. 2019
    Budget Restricted Incremental Learning with Pre-Trained Convolutional Neural Networks and Binary Associative MemoriesGhouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia … Michel JézéquelJournal of Signal Processing Systems · IMT Atlantique · Université de Bretagne Occidentale · +2
  48. 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
  49. 2019
    Facilitating Bayesian Continual Learning by Natural Gradients and Stein GradientsYu Chen, Tom Diethe, Neil D. LawrencearXiv · University of Bristol · Amazon (Germany)
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  50. 2019
    Synaptic weight decay with selective consolidation enables fast learning without catastrophic forgettingPascal Leimer, Michael H. Herzog, Walter SennbioRxiv · University of Bern · École Polytechnique Fédérale de Lausanne
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.