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.

117 papers of 8,653 · showing 101–117Sort Recent · Most cited
  1. 2018
    End-to-End Incremental LearningFrancisco M. Castro, Manuel J. Marín‐Jiménez, Nicolás Guil … Karteek AlahariECCV · Universidad de Málaga · University of Córdoba · +5
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  2. 2018
    Lifelong Machine Learning, Second EditionZhiyuan Chen, Bing LiuMachine Learning · Google (United States) · University of Illinois Chicago
  3. 2018
    Lifelong Learning via Progressive Distillation and RetrospectionSaihui Hou, Xinyu Pan, Chen Change Loy … Dahua LinECCV · University of Science and Technology of China · Chinese University of Hong Kong · +1
  4. 2018PDF ↗
  5. 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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  6. 2018
    Enriching Translation-Based Knowledge Graph Embeddings Through Continual LearningHyun-Je Song, Seong-Bae ParkIEEE Access · Naver (South Korea) · Kyung Hee University
  7. 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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  8. 2018
    The effects of morphology and fitness on catastrophic interferenceJoshua Powers, Sam Kriegman, Josh BongardThe 2018 Conference on Artificial Life · University of Vermont · Morpho (United States)
  9. 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
  10. 2018
    Continuous Learning in a Hierarchical Multiscale Neural NetworkThomas Wolf, Julien Chaumond, Clément DelangueACL · Central European University · Bio Signal Group (United States)
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  11. 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
  12. 2018
    Investigation of Incremental Learning as Temporal Feature ExtractionShoya Matsumori, Yuki Abe, Masahiko Osawa, Michita ImaiProcedia Computer Science · Keio University · Japan Society for the Promotion of Science
  13. 2018
    Alleviating Catastrophic Forgetting with Modularity for Continuously Learning Linked Open DataLu Chen, Masayuki MurataInternational Journal of Computer Theory and Engineering · The University of Osaka
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  14. 2018
    Lifelong Learning with the Feedback-loop between Emotions and Actions via Internal RewardDharani Punithan, Byoung‐Tak ZhangProcedia Computer Science · Seoul National University
  15. 2018
    FearNet: Brain-Inspired Model for Incremental LearningRonald Kemker, Christopher KananICLR
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  16. 2018
    Modular Continual Learning in a Unified Visual EnvironmentKevin Feigelis, Blue Sheffer, Daniel YaminsICLR · Rutgers, The State University of New Jersey · Stanford University
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  17. 2018
    Lifelong Learning with Dynamically Expandable NetworksJaehong Yoon, Eunho Yang, Jeongtae Lee, Sung Ju HwangICLR · Korea Advanced Institute of Science and Technology
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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.