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.

17 papers of 8,653Sort Recent · Most cited
  1. 2021
    Continual Learning with Knowledge Transfer for Sentiment ClassificationZixuan Ke, Bing Liu, Hao Wang, Lei ShuSpringer LNCS · University of Illinois Chicago · Southwest Jiaotong University
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  2. 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
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  3. 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
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  4. 2021
    Continual Learning with Dual RegularizationsXuejun Han, Yuhong GuoSpringer LNCS · Carleton University · Canadian Institute for Advanced Research
  5. 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
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  6. 2021
    DRILL: Dynamic Representations for Imbalanced Lifelong LearningKyra Ahrens, Fares Abawi, Stefan WermterSpringer LNCS · Universität Hamburg
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  7. 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
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  8. 2021
    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
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  9. 2021
    Continual Learning for Object Classification: A Modular ApproachDaniel Z. Turner, Pedro J. S. Cardoso, João M. F. RodriguesSpringer LNCS · University of Algarve
  10. 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
  11. 2021
    Continual Learning with Laplace Operator Based Node-Importance Dynamic Architecture Neural NetworkZhiyuan Li, Ming Meng, Yifan He, Yihao LiaoSpringer LNCS · Hangzhou Dianzi University
  12. 2021
    Measuring Catastrophic Forgetting in Visual Question AnsweringClaudio Greco, Barbara Plank, Raquel Fernández, Raffaella BernardiSpringer LNCS · University of Trento · IT University of Copenhagen · +1
  13. 2021
    Continual Learning of 3D Point Cloud GeneratorsMichał Sadowski, Karol J. Piczak, Przemysław Spurek, T. P. TrzcinskiSpringer LNCS · Jagiellonian University · Warsaw University of Technology
  14. 2021
    Principal Gradient Direction and Confidence Reservoir Sampling for Continual LearningZhiyi Chen, Tong LinSpringer LNCS · Georgia Institute of Technology · Peking University · +1
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  15. 2021
  16. 2021
    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
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  17. 2021
    Dynamic Mitigation of Catastrophic Forgetting Using the Sampling NetworkDae Yong Hong, Yan Li, Byeong‐Seok ShinSpringer LNCS · Inha University
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.