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.

7 papers of 11,817Sort Recent · Most cited
  1. 2020
    Generative Feature Replay with Orthogonal Weight Modification for Continual LearningGehui Shen, Song Zhang, Xiang Chen, Zhihong DengIJCNN · Peking University
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  2. 2020
    Bilevel Continual LearningAmmar Shaker, Francesco Alesiani, Shujian Yu, Wenzhe YinIJCNN · Heidelberg University
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  3. 2020
    A Biologically Plausible Audio-Visual Integration Model for Continual LearningWenjie Chen, Fengtong Du, Ye Wang, Lihong CaoIJCNN · Communication University of China
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  4. 2020
    Catastrophic forgetting and mode collapse in GANsHoang Thanh-Tung, Truyen TranIJCNN · Deakin University · Allen Institute for Artificial Intelligence
  5. 2020
    OvA-INN: Continual Learning with Invertible Neural NetworksGuillaume Hocquet, Olivier Bichler, Damien QuerliozIJCNN · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Intégration des Systèmes et des Technologies · +2
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  6. 2020
    GPU-based State Adaptive Random Forest for Evolving Data StreamsOcean Wu, Yun Sing Koh, Giovanni RusselloIJCNN · University of Auckland
  7. 2020
    Online Knowledge Acquisition with the Selective Inherited ModelXiaocong Du, Shreyas Kolala Venkataramanaiah, Zheng Li … Yu CaoIJCNN · Arizona State University · Oak Ridge National Laboratory
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.