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.

21 papers of 11,817Sort Recent · Most cited
  1. 2023
    ViT-MVT: A Unified Vision Transformer Network for Multiple Vision TasksTao Xie, Kun Dai, Zhiqiang Jiang … Li-jun ZhaoTNNLS
  2. 2023
    Wake-Sleep Consolidated LearningAmelia Sorrenti, Giovanni Bellitto, F. Salanitri … C. SpampinatoTNNLS
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  3. 2023
    A Survey on Knowledge Editing of Neural NetworksVittorio Mazzia, Alessandro Pedrani, Andrea Caciolai … Davide BernardiTNNLS
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  4. 2023
    When Broad Learning System Meets Label Noise Learning: A Reweighting Learning FrameworkLicheng Liu, Junhao Chen, Bin Yang … C. L. Philip ChenTNNLS
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  6. 2023
    HPCR: Holistic Proxy-Based Contrastive Replay for Online Continual LearningHuiwei Lin, Shanshan Feng, Baoquan Zhang … Yunming YeTNNLS
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  7. 2023
  8. 2023
    Online Active Continual Learning for Robotic Lifelong Object RecognitionXiangli Nie, Zhiguang Deng, Mingdong He … Z. TangTNNLS
  9. 2023
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  11. 2023
    Lifelong Learning With Cycle Memory NetworksJian Peng, Dingqi Ye, Bo Tang … Haifeng LiTNNLS
  12. 2023
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  14. 2023
    Few-Shot Continual Learning via Flat-to-Wide ApproachesM. A. Ma'sum, Mahardhika Pratama, Lin Liu … Ryszard KowalczykTNNLS
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  15. 2023
    Multiagent Continual Coordination via Progressive Task ContextualizationLei Yuan, Lihe Li, Ziqian Zhang … Yang YuTNNLS
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  20. 2023
    Robust and Rotation-Equivariant Contrastive LearningGairui Bai, Wei Xi, Xiaopeng Hong … Songwen ZhaoTNNLS
  21. 2023PDF ↗
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.