Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

490 papers of 11,817 · showing 51–100Sort Recent · Most cited
  1. 2019
    Class-incremental Learning via Deep Model ConsolidationJunting Zhang, Jie Zhang, Shalini Ghosh … C.‐C. Jay KuoWACV · University of Southern California · California Southern University · +3
    PDF ↗
  2. 2019
    Model primitives for hierarchical lifelong reinforcement learningBohan Wu, Jayesh K. Gupta, Mykel J. KochenderferAutonomous Agents and Multi-Agent Systems · Columbia University · Stanford University
  3. 2019
    Efficient Continual Learning in Neural Networks with Embedding RegularizationJary Pomponi, Simone Scardapane, Vincenzo Lomonaco, Aurelio UnciniNeurocomputing · Sapienza University of Rome · University of Bologna
    PDF ↗
  4. 2019PDF ↗
  5. 2019
    REMIND Your Neural Network to Prevent Catastrophic ForgettingTyler L. Hayes, Kushal Kafle, Robik Shrestha … Christopher KananSpringer LNCS · Rochester Institute of Technology · Adobe Systems (United States) · +2
    PDF ↗
  6. 2019
    Continual Adaptation for Efficient Machine CommunicationRobert D. Hawkins, Minae Kwon, Dorsa Sadigh, Noah D. GoodmanCoNLL · Princeton University · Department of Physics, Mathematics and Informatics · +1
    PDF ↗
  7. 2019
    Cellular Innovation of the Cyanobacterial Heterocyst by the Adaptive Loss of Plasticity.Scott R. Miller, Reid Longley, Patrick R. Hutchins, Thorsten BauersachsCurrent Biology · University of Montana · Christian-Albrechts-Universität zu Kiel
  8. 2019
    Moving Towards Open Set Incremental Learning: Readily Discovering New AuthorsJustin Leo, Jugal KalitaAdvances in intelligent systems and computing · University of Colorado Colorado Springs
    PDF ↗
  9. 2019
    Morphology dictates learnability in neural controllersJoshua Powers, Ryan Grindle, Sam Kriegman … Josh BongardThe 2020 Conference on Artificial Life · University of Vermont
    PDF ↗
  10. 2019
    Continual Learning of Image Translation Networks Using Task-Dependent Weight Selection MasksMatsumoto Asato, ‪Keiji Yanai‬Springer LNCS · University of Electro-Communications
  11. 2019
    Side-Tuning: Network Adaptation via Additive Side NetworksJeffrey O. Zhang, Alexander F. Sax, Amir Zamir … Jitendra MalikarXiv
    PDF ↗
  12. 2019
    Direction Concentration Learning: Enhancing Congruency in Machine LearningYan Luo, Yongkang Wong, Mohan Kankanhalli, Qi ZhaoTPAMI · University of Minnesota · National University of Singapore
    PDF ↗
  13. 2019PDF ↗
  14. 2019
    Human role in the modern robotic reproduction developmentT. S. Kolmykova, Ekaterina MerzlyakovaEconomic Annals-ХХI
  15. 2019
    A Cross‐Dimension Annotations Method for 3D Structural Facial Landmark ExtractionXun Gong, Ping Chen, Zhemin Zhang … Xin LiComputer Graphics Forum · Southwest Jiaotong University · Tsinghua Sichuan Energy Internet Research Institute · +1
  16. 2019
    A comparative study of general fuzzy min-max neural networks for pattern classification problemsThanh Tung Khuat, Bogdan GabryśNeurocomputing · University of Technology Sydney
    PDF ↗
  17. 2019
    RPGAN: GANs Interpretability via Random RoutingAndrey Voynov, Artem BabenkoarXiv
    PDF ↗
  18. 2019
    A COMPACT OPTIMAL LEARNING MACHINEKanathip Sae-pae, K. WoraratpanyaMalaysian Journal of Computer Science
  19. 2019
    Continuous Meta-Learning without TasksJ. Michael Harrison, Apoorva Sharma, Chelsea Finn, Marco PavonearXiv · Stanford University · University of California, Berkeley
    PDF ↗
  20. 2019
    Continual learning for image classification. (Apprentissage continu pour la classification des images)Anuvabh DuttUniversité Grenoble Alpes (ComUE) · Université Grenoble Alpes
  21. 2019
    Decentralized Attention-based Personalized Human Mobility PredictionZipei Fan, Xuan Song, Renhe Jiang … Ryosuke ShibasakiACM on Interactive Mobile Wearable and Ubiquitous Technol… · Southern University of Science and Technology · The University of Tokyo
  22. 2019PDF ↗
  23. 2019
    Continual Learning for RoboticsTimothée Lesort, Vincenzo Lomonaco, Andrei Stoian … Natalia Díaz-RodríguezInformation Fusion · Thales (Portugal) · Institut national de recherche en sciences et technologies du numérique · +3
    PDF ↗
  24. 2019
    Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement LearningJoão G. Ribeiro, Francisco S. Melo, João DiasEPiC series in computing · Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento
    PDF ↗
  25. 2019
    Reducing Catastrophic Forgetting in Modular Neural Networks by Dynamic Information BalancingMohammed Amer, Tomás MaularXiv · University of Nottingham Malaysia Campus
    PDF ↗
  26. 2019PDF ↗
  27. 2019
    Learning Sparse Representations Incrementally in Deep Reinforcement LearningJ. Fernando Hernandez-Garcia, Richard S. SuttonarXiv · University of Alberta
    PDF ↗
  28. 2019
    Random Path Selection for Continual LearningJathushan Rajasegaran, Munawar Hayat, Salman Hameed Khan … Ling ShaoNeurIPS
  29. 2019
    Bayesian Structure Adaptation for Continual LearningAbhishek Kumar, Sunabha Chatterjee, Piyush RaiarXiv · Indian Institute of Technology Kanpur
    PDF ↗
  30. 2019
    Nonparametric Bayesian Structure Adaptation for Continual LearningAbhishek Kumar, Sunabha Chatterjee, Piyush RaiarXiv
  31. 2019
    Regularization Shortcomings for Continual LearningTimothée Lesort, Andrei Stoian, Filliat, DavidarXiv · École d'Ingénieurs en Chimie et Sciences du Numérique · Thales (Portugal)
    PDF ↗
  32. 2019
    Hierarchical Indian buffet neural networks for Bayesian continual learningSamuel Kessler, Vu Nguyen, Stefan Zohren, Stephen RobertsUAI · University of Oxford · Science Oxford
    PDF ↗
  33. 2019
    Learning to Recommend via Meta Parameter PartitionLiang Zhao, Yang Wang, Daxiang Dong, Hao TianarXiv · Baidu (China)
    PDF ↗
  34. 2019
    Indian Buffet Neural Networks for Continual LearningSamuel Kessler, Vu Nguyen, S. Zohren, Stephen J. RobertsarXiv
  35. 2019
    Overcoming Catastrophic Forgetting by Bayesian Generative RegularizationPatrick H. Chen, Wei Wei, Cho‐Jui Hsieh, Bo DaiICML · University of California, Los Angeles
    PDF ↗
  36. 2019
    Overcoming Catastrophic Forgetting by Generative RegularizationPatrick H. Chen, Wei Wei, Cho-Jui Hsieh, Bo DaiarXiv
  37. 2019
    Incremental learning for the detection and classification of GAN-generated imagesFrancesco Marra, Cristiano Saltori, Giulia Boato, Luisa VerdolivaInternational Workshop on Information Forensics and Security · Federico II University Hospital · University of Trento
    PDF ↗
  38. 2019
    DCIGAN: A Distributed Class-Incremental Learning Method Based on Generative Adversarial NetworksHongtao Guan, Yijie Wang, Xingkong Ma, Yongmou LiIEEE Intl Conf on Parallel & Distributed Processing w… · National University of Defense Technology
  39. 2019
    Feature Selection for Data Classification based on Binary Brain Storm OptimizationFarhad Pourpanah, Ran Wang, Xizhao WangIEEE 6th International Conference on Cloud Computing and… · Shenzhen University
  40. 2019
    Frosting Weights for Better Continual TrainingXiaofeng Zhu, Feng Liu, Goce Trajcevski, Dingding WangICML · Northwestern University · Florida Atlantic University · +1
    PDF ↗
  41. 2019
    A Scalable Data Augmentation and Training Pipeline for Logo DetectionHan Guo, Viswanathan Swaminathan, Saayan MitraIEEE International Symposium on Multimedia (ISM) · Adobe Systems (United States)
  42. 2019
    Visual Image Classification Based on Auto-Associative MemoryDaoliang He, Yanjiang Wang, Mingyue Gao … Linxia XiaoIEEE Symposium Series on Computational Intelligence (SSCI) · China University of Petroleum, East China
  43. 2019
    AutoML @ NeurIPS 2018 challenge: Design and ResultsHugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon … Qiang YangMachine Learning · Gleason (United States) · National Institute of Astrophysics, Optics and Electronics · +8
    PDF ↗
  44. 2019
    GRIm-RePR: Prioritising Generating Important Features for Pseudo-RehearsalC. Atkinson, B. McCane, Lech Szymanski, A. RobinsarXiv
    PDF ↗
  45. 2019
    Machine learning for streaming data: state of the art, challenges, and opportunitiesHeitor Murilo Gomes, Jesse Read, Albert Bifet … João GamaACM SIGKDD Explorations Newsletter · École Polytechnique · Universidade do Porto
  46. 2019
    Continual Learning with Adaptive Weights (CLAW)Tameem Adel, Han Zhao, Richard E. TurnerICLR
    PDF ↗
  47. 2019
    Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-meansAndrea Agostinelli, Kai Arulkumaran, Marta Sarrico … Anil A. BharatharXiv · Imperial College London
    PDF ↗
  48. 2019PDF ↗
  49. 2019
    Online Learned Continual Compression with Stacked Quantization ModuleLucas Caccia, Eugene Belilovsky, M. Caccia, Joëlle PineauarXiv · McGill University · Meta (Israel)
    PDF ↗
  50. 2019PDF ↗
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 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.