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.

229 papers of 8,653 · showing 151–200Sort Recent · Most cited
  1. 2021
    Continual Learning with Neuron Activation ImportanceSohee Kim, Seungkyu LeeSpringer LNCS · Kyung Hee University
    PDF ↗
  2. 2020
    Modular-Relatedness for Continual LearningAmmar Shaker, Francesco Alesiani, Shujian YuSpringer LNCS · UiT The Arctic University of Norway
    PDF ↗
  3. 2021
    Transfer and Continual Supervised Learning for Robotic Grasping Through Grasping FeaturesLuca Monorchio, Marco Capotondi, Mario Corsanici … Francesco PujaSpringer LNCS · Sapienza University of Rome
  4. 2021
    Discriminative Distillation to Reduce Class Confusion in Continual LearningChanghong Zhong, Zhiying Cui, Wei‐Shi Zheng … Ruixuan WangSpringer LNCS · Sun Yat-sen University · Key Laboratory of Guangdong Province
    PDF ↗
  5. 2021
    Self-supervised Novelty Detection for Continual Learning: A Gradient-Based Approach Boosted by Binary ClassificationJingbo Sun, Li Yang, Jiaxin Zhang … Yu CaoSpringer LNCS · Arizona State University · Oak Ridge National Laboratory · +1
  6. 2022
    Continual Learning Based on Knowledge Distillation and Representation LearningXiuyan Chen, Jian–wei Liu, Wentao LiSpringer LNCS · China University of Petroleum, Beijing
  7. 2022
    Continual Learning by Task-Wise Shared Hidden Representation AlignmentXu-hui Zhan, Jian–wei Liu, Ya-nan HanSpringer LNCS · China University of Petroleum, Beijing
  8. 2022
    Partially Relaxed Masks for Knowledge Transfer Without Forgetting in Continual LearningTatsuya Konishi, Mori Kurokawa, Chihiro Ono … Bing LiuSpringer LNCS · KDDI Research (Japan) · University of Illinois Chicago
  9. 2021
    On Regret Bounds for Continual Single-Index LearningThe Tien MaiSpringer LNCS · Norwegian University of Science and Technology
    PDF ↗
  10. 2021
    The Role of Bio-Inspired Modularity in General LearningRachel A. StClair, William Edward Hahn, Elan BarenholtzSpringer LNCS · Florida Atlantic University
    PDF ↗
  11. 2021
    Continual Learning with Knowledge Transfer for Sentiment ClassificationZixuan Ke, Bing Liu, Hao Wang, Lei ShuSpringer LNCS · University of Illinois Chicago · Southwest Jiaotong University
    PDF ↗
  12. 2021
    Studying Catastrophic Forgetting in Neural Ranking ModelsJesús Lovón-Melgarejo, Laure Soulier, Karen Pinel-Sauvagnat, Lynda TamineSpringer LNCS · Université Toulouse III - Paul Sabatier · Institut de Recherche en Informatique de Toulouse · +4
    PDF ↗
  13. 2021
    Learning without Forgetting for 3D Point Cloud ObjectsTownim Faisal Chowdhury, Mahira Jalisha, Ali Cheraghian, Shafin RahmanSpringer LNCS · North South University · Australian National University · +2
    PDF ↗
  14. 2021
    Continual Learning with Dual RegularizationsXuejun Han, Yuhong GuoSpringer LNCS · Carleton University · Canadian Institute for Advanced Research
  15. 2020
    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 ↗
  16. 2021
    DRILL: Dynamic Representations for Imbalanced Lifelong LearningKyra Ahrens, Fares Abawi, Stefan WermterSpringer LNCS · Universität Hamburg
    PDF ↗
  17. 2020
    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 ↗
  18. 2022
    Bilevel Online Deep Learning in Non-stationary EnvironmentYa-nan Han, Jian–wei Liu, Bing-biao Xiao … Xiong-lin LuoSpringer LNCS · China University of Petroleum, Beijing
    PDF ↗
  19. 2021
    Continual Learning for Object Classification: A Modular ApproachDaniel Z. Turner, Pedro J. S. Cardoso, João M. F. RodriguesSpringer LNCS · University of Algarve
  20. 2021
    Continual Learning for Multi-camera RelocalisationAldrich A. Cabrera-Ponce, Manuel Martín-Ortíz, José Martínez-CarranzaSpringer LNCS · Benemérita Universidad Autónoma de Puebla · University of Bristol · +1
  21. 2021
    Continual Learning with Laplace Operator Based Node-Importance Dynamic Architecture Neural NetworkZhiyuan Li, Ming Meng, Yifan He, Yihao LiaoSpringer LNCS · Hangzhou Dianzi University
  22. 2019
    Measuring Catastrophic Forgetting in Visual Question AnsweringClaudio Greco, Barbara Plank, Raquel Fernández, Raffaella BernardiSpringer LNCS · University of Trento · IT University of Copenhagen · +1
  23. 2021
    Continual Learning of 3D Point Cloud GeneratorsMichał Sadowski, Karol J. Piczak, Przemysław Spurek, T. P. TrzcinskiSpringer LNCS · Jagiellonian University · Warsaw University of Technology
  24. 2021
    Principal Gradient Direction and Confidence Reservoir Sampling for Continual LearningZhiyi Chen, Tong LinSpringer LNCS · Georgia Institute of Technology · Peking University · +1
    PDF ↗
  25. 2021
  26. 2021
    Dynamic Mitigation of Catastrophic Forgetting Using the Sampling NetworkDae Yong Hong, Yan Li, Byeong‐Seok ShinSpringer LNCS · Inha University
  27. 2020
    An Empirical Study of Incremental Learning in Neural Network with Noisy Training SetShovik Ganguly, Atrayee Chatterjee, Debasmita Bhoumik, Ritajit MajumdarSpringer LNCS · University of Calcutta · Indian Statistical Institute
    PDF ↗
  28. 2020
    GDumb: A Simple Approach that Questions Our Progress in Continual LearningAmeya Prabhu, Philip H. S. Torr, Puneet K. DokaniaSpringer LNCS · University of Oxford
  29. 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 ↗
  30. 2020
    Adversarial Continual LearningSayna Ebrahimi, Franziska Meier, Roberto Calandra … Marcus RohrbachSpringer LNCS · Berkeley College · Meta (United States) · +2
    PDF ↗
  31. 2020
    Memory-Efficient Incremental Learning Through Feature AdaptationAhmet İşcen, Jeffrey Zhang, Svetlana Lazebnik, Cordelia SchmidSpringer LNCS · University of Illinois Urbana-Champaign
    PDF ↗
  32. 2020
    Topology-Preserving Class-Incremental LearningXiaoyu Tao, Xinyuan Chang, Xiaopeng Hong … Yihong GongSpringer LNCS · Xi'an Jiaotong University · Peng Cheng Laboratory
  33. 2020
    Imbalanced Continual Learning with Partitioning Reservoir SamplingChris Dongjoo Kim, Jinseo Jeong, Gunhee KimSpringer LNCS · Seoul National University
    PDF ↗
  34. 2020
    More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental LearningYu Liu, Sarah Parisot, Greg Slabaugh … Tinne TuytelaarsSpringer LNCS · KU Leuven · Huawei Technologies (China) · +1
    PDF ↗
  35. 2020
    Reparameterizing Convolutions for Incremental Multi-Task Learning without Task InterferenceMenelaos Kanakis, David Brüggemann, Suman Saha … Luc Van GoolSpringer LNCS · ETH Zurich · KU Leuven
    PDF ↗
  36. 2020
    Learning latent representations across multiple data domains using Lifelong VAEGANFei Ye, Adrian G. BorşSpringer LNCS · University of York
    PDF ↗
  37. 2020
    Online Continual Learning under Extreme Memory ConstraintsEnrico Fini, Stéphane Lathuilière, Enver Sangineto … Elisa RicciSpringer LNCS · University of Trento · Télécom Paris · +2
    PDF ↗
  38. 2020
    Class-Incremental Domain AdaptationJogendra Nath Kundu, Rahul Venkatesh, Naveen Venkat … R. Venkatesh BabuSpringer LNCS · Indian Institute of Science Bangalore
    PDF ↗
  39. 2021
    Piggyback GAN: Efficient Lifelong Learning for Image Conditioned GenerationMengyao Zhai, Lei Chen, Jiawei He … Greg MoriSpringer LNCS · Simon Fraser University · Collège Boréal
    PDF ↗
  40. 2020
    Incremental Few-Shot Meta-learning via Indirect Discriminant AlignmentQing Liu, Orchid Majumder, Alessandro Achille … Stefano SoattoSpringer LNCS · Johns Hopkins University · Amazon (United States)
  41. 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 ↗
  42. 2019
    Continual Learning of Image Translation Networks Using Task-Dependent Weight Selection MasksMatsumoto Asato, ‪Keiji Yanai‬Springer LNCS · University of Electro-Communications
  43. 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
  44. 2018
    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 ↗
  45. 2018
    Revisiting Distillation and Incremental Classifier LearningKhurram Javed, Faisal ShafaitSpringer LNCS · National University of Sciences and Technology
    PDF ↗
  46. 2018
    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 ↗
  47. 2018
    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 ↗
  48. 2019
    A Study on Catastrophic Forgetting in Deep LSTM NetworksMonika Schak, Alexander GepperthSpringer LNCS · Fulda University of Applied Sciences
  49. 2019
    Continual Learning Exploiting Structure of Fractal Reservoir ComputingTaisuke Kobayashi, Toshiki SuginoSpringer LNCS · Nara Institute of Science and Technology
  50. 2019
    Strategies for Improving Single-Head Continual Learning PerformanceAlaa El Khatib, Fakhri KarraySpringer LNCS · University of Waterloo
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.