Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

11 papers of 6,984Sort Recent · Most cited
  1. 2026
    FOCUS: Frequency-Optimized Conditioning of diffUSion models for mitigating catastrophic forgetting during test-time adaptationGabriel Tjio, Jie Zhang, Xulei Yang … Qing GuoMachine Vision and Applications · Agency for Science, Technology and Research · Nanyang Technological University · +4
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  2. 2026PDF ↗
  3. 2025
    Uni-IL: Unified Incremental Learning of Vision-Language Models via Mixture of Attribute-Guided ExpertsFanyu Meng, Yufeng Zhan, Jie Zhang … Yuanqing XiaACM International Conference on Multimedia in Asia · Beijing Institute of Technology · Hong Kong University of Science and Technology · +1
  4. 2025
    A Unified Gradient-based Framework for Task-agnostic Continual Learning-UnlearningZhehao Huang, Xinwen Cheng, Jie Zhang … Xiaolin HuangarXiv
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  5. 2023
    TARGET: Federated Class-Continual Learning via Exemplar-Free DistillationJie Zhang, Chen Chen, Weiming Zhuang, Lingjuan LyuICCV · ETH Zurich · Sony Corporation (United States)
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  6. 2023PDF ↗
  7. 2023
    Addressing Catastrophic Forgetting in Federated Class-Continual LearningJie Zhang, Chen Chen, Weiming Zhuang, Ling-Juan LvarXiv
  8. 2020
    Class-incremental Learning via Deep Model ConsolidationJunting Zhang, Jie Zhang, Shalini Ghosh … C.‐C. Jay KuoWACV · University of Southern California · California Southern University · +3
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  9. 2020
    Regularize, Expand and Compress: NonExpansive Continual LearningJie Zhang, Junting Zhang, Shalini Ghosh … Yalin WangWACV · University of Southern California · Arizona State University · +1
  10. 2019
    MUSE-RNN: A Multilayer Self-Evolving Recurrent Neural Network for Data Stream ClassificationMonidipa Das, Mahardhika Pratama, Septiviana Savitri, Jie ZhangICDM · Nanyang Technological University
  11. 2019PDF ↗
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.