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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.

112 papers of 8,653 · showing 101–112Sort Recent · Most cited
Springer LNCS Clear all
  1. 2019
    Central-Diffused Instance Generation Method in Class Incremental LearningMing-Yu Liu, Yijie WangSpringer LNCS · National University of Defense Technology
  2. 2018
    Catastrophic Forgetting: Still a Problem for DNNsBenedikt Pfülb, Alexander Gepperth, Syahrul Afzal Che Abdullah, Axel KilianSpringer LNCS · Fulda University of Applied Sciences
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  3. 2018
    Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network AugmentationAbel Zacarias, Luı́s A. AlexandreSpringer LNCS · University of Beira Interior · Instituto de Telecomunicações
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  4. 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
  5. 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
  6. 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
  7. 2016
    Comparing Incremental Learning Strategies for Convolutional Neural NetworksVincenzo Lomonaco, Davide MaltoniSpringer LNCS · University of Bologna
  8. 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
  9. 2010
    An Incremental Learning Method for Neural Networks Based on Sensitivity AnalysisBeatriz Pérez‐Sánchez, Óscar Fontenla-Romero, Bertha Guijarro‐BerdiñasSpringer LNCS · Universidade da Coruña
  10. 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
  11. 2008
    Supervised Incremental Learning with the Fuzzy ARTMAP Neural NetworkJean-François Connolly, Éric Granger, Robert SabourinSpringer LNCS · École de Technologie Supérieure
  12. 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. 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.