Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

131 papers of 7,070 · showing 101–131Sort Recent · Most cited
  1. 2021
    Explaining How Deep Neural Networks Forget by Deep VisualizationGiang V. Nguyen, Chen Shuan, Tae Joon Jun, Daeyoung KimSpringer LNCS · Korea Advanced Institute of Science and Technology · Ansan University
    PDF ↗
  2. 2021
    DRILL: Dynamic Representations for Imbalanced Lifelong LearningKyra Ahrens, Fares Abawi, Stefan WermterSpringer LNCS · Universität Hamburg
    PDF ↗
  3. 2021
    Class-incremental Learning with Rectified Feature-Graph PreservationCheng-Hsun Lei, Yi-Hsin Chen, Wen-Hsiao Peng, Wei-Chen ChiuSpringer LNCS · National Yang Ming Chiao Tung University
    PDF ↗
  4. 2021
    Continual Learning with Laplace Operator Based Node-Importance Dynamic Architecture Neural NetworkZhiyuan Li, Ming Meng, Yifan He, Yihao LiaoSpringer LNCS · Hangzhou Dianzi University
  5. 2021
    Measuring Catastrophic Forgetting in Visual Question AnsweringClaudio Greco, Barbara Plank, Raquel Fernández, Raffaella BernardiSpringer LNCS · University of Trento · IT University of Copenhagen · +1
  6. 2021
    Continual Learning of 3D Point Cloud GeneratorsMichał Sadowski, Karol J. Piczak, Przemysław Spurek, T. P. TrzcinskiSpringer LNCS · Jagiellonian University · Warsaw University of Technology
  7. 2021
    Principal Gradient Direction and Confidence Reservoir Sampling for Continual LearningZhiyi Chen, Tong LinSpringer LNCS · Georgia Institute of Technology · Peking University · +1
    PDF ↗
  8. 2020
    Active Class Incremental Learning for Imbalanced DatasetsEden Belouadah, Adrian Popescu, Umang Aggarwal, Léo SaciSpringer LNCS · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Intégration des Systèmes et des Technologies · +1
    PDF ↗
  9. 2020
    Continual Learning of Image Translation Networks Using Task-Dependent Weight Selection MasksMatsumoto Asato, ‪Keiji Yanai‬Springer LNCS · University of Electro-Communications
  10. 2020
    An Incremental Learning Network Model Based on Random Sample Distribution FittingWencong Wang, Lan Huang, Hao Liu … Kangping WangSpringer LNCS · Jilin University · Jilin Province Science and Technology Department
  11. 2019
    DeeSIL: Deep-Shallow Incremental LearningEden Belouadah, Adrian PopescuSpringer LNCS · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Intégration des Systèmes et des Technologies
    PDF ↗
  12. 2019
    Revisiting Distillation and Incremental Classifier LearningKhurram Javed, Faisal ShafaitSpringer LNCS · National University of Sciences and Technology
    PDF ↗
  13. 2019
    Adding New Tasks to a Single Network with Weight Trasformations using Binary MasksMassimiliano Mancini, Elisa Ricci, Barbara Caputo, Samuel Rota BulòSpringer LNCS · Fondazione Bruno Kessler · Sapienza University of Rome · +2
    PDF ↗
  14. 2019
    Marginal Replay vs Conditional Replay for Continual LearningTimothée Lesort, Alexander Gepperth, Andrei Stoian, David FilliatSpringer LNCS · École Nationale Supérieure de Techniques Avancées · Thales (France) · +1
    PDF ↗
  15. 2019
    A Study on Catastrophic Forgetting in Deep LSTM NetworksMonika Schak, Alexander GepperthSpringer LNCS · Fulda University of Applied Sciences
  16. 2019
    Simplified Computation and Interpretation of Fisher Matrices in Incremental Learning with Deep Neural NetworksAlexander Gepperth, Florian WiechSpringer LNCS · Fulda University of Applied Sciences
  17. 2019
    Overcoming Catastrophic Interference with Bayesian Learning and Stochastic Langevin DynamicsMikhail Leontev, Alexander Mikheev, Kirill Sviatov, Sergey SukhovSpringer LNCS · Ulyanovsk State University · Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences · +1
  18. 2019
    Lifelong Learning Starting From ZeroClaes Strannegård, Herman Carlström, Niklas Engsner … Morteza Haghir ChehreghaniSpringer LNCS · Chalmers University of Technology
    PDF ↗
  19. 2019
    Central-Diffused Instance Generation Method in Class Incremental LearningMing-Yu Liu, Yijie WangSpringer LNCS · National University of Defense Technology
  20. 2019
    Transfer Learning with Sparse Associative MemoriesQuentin Jodelet, Vincent Gripon, Masafumi HagiwaraSpringer LNCS · Keio University · IMT Atlantique
    PDF ↗
  21. 2018
    Catastrophic Forgetting: Still a Problem for DNNsBenedikt Pfülb, Alexander Gepperth, Syahrul Afzal Che Abdullah, Axel KilianSpringer LNCS · Fulda University of Applied Sciences
    PDF ↗
  22. 2018
    Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network AugmentationAbel Zacarias, Luı́s A. AlexandreSpringer LNCS · University of Beira Interior · Instituto de Telecomunicações
    PDF ↗
  23. 2018
    A Broad Neural Network Structure for Class Incremental LearningWenzhang Liu, Haiqin Yang, Yuewen Sun, Changyin SunSpringer LNCS · Southeast University · Hang Seng University of Hong Kong
  24. 2018
    Overcoming Catastrophic Forgetting with Self-adaptive IdentifiersFangzhou Xiong, Zhiyong Liu, Xu YangSpringer LNCS · Shandong Institute of Automation · University of Chinese Academy of Sciences · +2
  25. 2017
    Continual and One-Shot Learning Through Neural Networks with Dynamic External MemoryBenno Lüders, Mikkel Schläger, Aleksandra Korach, Sebastian RisiSpringer LNCS · IT University of Copenhagen
  26. 2016
    Comparing Incremental Learning Strategies for Convolutional Neural NetworksVincenzo Lomonaco, Davide MaltoniSpringer LNCS · University of Bologna
  27. 2016
    Analytical Incremental Learning: Fast Constructive Learning Method for Neural NetworkSyukron Abu Ishaq Alfarozi, Noor Akhmad Setiawan, Teguh Bharata Adji … Masanori SugimotoSpringer LNCS · Universitas Gadjah Mada · King Mongkut's Institute of Technology Ladkrabang · +1
  28. 2010
    An Incremental Learning Method for Neural Networks Based on Sensitivity AnalysisBeatriz Pérez‐Sánchez, Óscar Fontenla-Romero, Bertha Guijarro‐BerdiñasSpringer LNCS · Universidade da Coruña
  29. 2009
    Weights Updated Voting for Ensemble of Neural Networks Based Incremental LearningJianjun Liu, Shengping Xia, Weidong Hu, Wenxian YuSpringer LNCS · National University of Defense Technology
  30. 2008
    Supervised Incremental Learning with the Fuzzy ARTMAP Neural NetworkJean-François Connolly, Éric Granger, Robert SabourinSpringer LNCS · École de Technologie Supérieure
  31. 2007
    Principles of Lifelong Learning for Predictive User ModelingAshish Kapoor, Eric HorvitzSpringer LNCS · Microsoft (United States)
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 written by someone who has published there, or cited a few hundred times. 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.