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.

9 papers of 11,817Sort Recent · Most cited
  1. 2026PDF ↗
  2. 2026PDF ↗
  3. 2026
    Resistive Memory based Efficient Machine Unlearning and Continual LearningNing Lin, Jichang Yang, Yangu He … Zhongrui WangarXiv
  4. 2024
    Self-Distillation Bridges Distribution Gap in Language Model Fine-TuningZhaorui Yang, Qian Liu, Tianyu Pang … Wei ChenACL
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  5. 2023
    Measuring and Mitigating Interference in Reinforcement LearningVincent Liu, Han Wang, Ruo Yu Tao … Martha WhiteCoLLAs
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  6. 2022
    Ask Question First for Enhancing Lifelong Language LearningHan Wang, Ruiliu Fu, Xuejun Zhang … Qingwei ZhaoCOLING
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  7. 2021
    Reminding the incremental language model via data-free self-distillationHan Wang, Ruiliu Fu, Chengzhang Li … Qingwei ZhaoApplied Intelligence · Chinese Academy of Sciences · Institute of Acoustics · +1
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  8. 2022
    RVAE-LAMOL: Residual Variational Autoencoder to Enhance Lifelong Language LearningHan Wang, Ruiliu Fu, Xuejun Zhang, Jun ZhouIJCNN · Chinese Academy of Sciences · Institute of Acoustics · +1
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  9. 2020
    Incremental transfer learning for video annotation via grouped heterogeneous sourcesHan Wang, Hao Song, Xinxiao Wu, Yunde JiaIET Computer Vision · Beijing Forestry University · Beijing Institute of Technology
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.