Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 11,817Sort Recent · Most cited
  1. 2020
    Simple Lifelong Learning MachinesJoshua T. Vogelstein, Jayanta Dey, Hayden S. Helm … Carey E. PriebeTPAMI · Johns Hopkins University · Baylor College of Medicine · +1
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  2. 2020
    Class-Incremental Learning: Survey and Performance Evaluation on Image ClassificationMarc Masana, Xialei Liu, Bartłomiej Twardowski … Joost van de WeijerTPAMI · Computer Vision Center
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  3. 2020
    Drinking From a Firehose: Continual Learning With Web-Scale Natural LanguageHexiang Hu, Ozan Şener, Fei Sha, Vladlen KoltunTPAMI · University of Southern California · Intel (United States)
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  4. 2020
    MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental LearningHanbin Zhao, Yongjian Fu, Mintong Kang … Xi LiTPAMI · Zhejiang University of Science and Technology · Huawei Technologies (China)
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  5. 2020
    Incremental Object Detection via Meta-LearningK J Joseph, Jathushan Rajasegaran, Salman Khan … Vineeth N BalasubramanianTPAMI · Indian Institute of Technology Hyderabad · Mohamed bin Zayed University of Artificial Intelligence
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  6. 2020
    One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic ForgettingMiao Zhang, Huiqi Li, Shirui Pan … Steven W. SuTPAMI · Beijing Institute of Technology · Monash University · +2
  7. 2020
    Novelty Detection and Online Learning for Chunk Data StreamsYi Wang, Yi Ding, Xiangjian He … Jiebo LuoTPAMI · Dalian University of Technology · University of Technology Sydney · +2
  8. 2020
    Explicit Filterbank Learning for Neural Image Style Transfer and Image ProcessingDongdong Chen, Lu Yuan, Jing Liao … Gang HuaTPAMI · University of Science and Technology of China · Microsoft (United States) · +1
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.