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. 2022
    Few-Shot Class-Incremental Learning for Named Entity RecognitionRui Wang, Tong Yu, Handong Zhao … Ricardo HenaoACL · Duke University · Adobe Systems (United States)
  2. 2021
    Class Incremental Online Streaming LearningSoumya Banerjee, Vinay Kumar Verma, Toufiq Parag … Vinay P. NamboodiriarXiv · Indian Institute of Technology Kanpur · Duke University · +1
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  3. 2021
    Knowledge Consolidation based Class Incremental Online Learning with Limited DataMohammed Asad Karim, Vinay Kumar Verma, Pravendra Singh … Piyush RaiIJCAI · Indian Institute of Technology Kanpur · Duke University · +2
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  4. 2021
    Efficient Feature Transformations for Discriminative and Generative Continual LearningVinay Kumar Verma, Kevin J Liang, Nikhil Mehta … Lawrence CarinCVPR · Duke University · Indian Institute of Technology Kanpur
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  5. 2021
    Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot LearningVinay Kumar Verma, Kevin J Liang, Nikhil Mehta, Lawrence CarinarXiv · Duke University
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  6. 2021
  7. 2021
    Mining Data Impressions From Deep Models as Substitute for the Unavailable Training DataGaurav Kumar Nayak, Konda Reddy Mopuri, Saksham Jain, Anirban ChakrabortyTPAMI · Indian Institute of Science Bangalore · Indian Institute of Technology Tirupati · +1
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  8. 2020
    GAN Memory with No ForgettingYulai Cong, Miaoyun Zhao, Jianqiao Li … Lawrence CarinNeurIPS · Duke University
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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. 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.