Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

61 papers of 8,653 · showing 51–61Sort Recent · Most cited
  1. 2024
  2. 2024
    RanDumb: Random Representations Outperform Online Continually Learned RepresentationsAmeya Prabhu, Shiven Sinha, Ponnurangam Kumaraguru … Puneet K. DokaniaNeurIPS
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  3. 2024
    DoFIT: Domain-aware Federated Instruction Tuning with Alleviated Catastrophic ForgettingBinqian Xu, Xiangbo Shu, Haiyang Mei … Jinhui TangNeurIPS
  4. 2024
    Persistence Homology Distillation for Semi-supervised Continual LearningYan Fan, Yu Wang, Pengfei Zhu … Pengfei ZhuNeurIPS
  5. 2024
    Label Delay in Online Continual LearningBotos Csaba, Wenxuan Zhang, Matthias Müller … Adel BibiNeurIPS
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  6. 2024
  7. 2024
    Model Sensitivity Aware Continual LearningZhenyi Wang, Heng HuangNeurIPS
  8. 2024
    Continual Learning with Global AlignmentXueying Bai, Jinghuan Shang, Yifan Sun, Niranjan BalasubramanianNeurIPS
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  9. 2024
  10. 2024
  11. 2024
    Adaptive Visual Scene Understanding: Incremental Scene Graph GenerationNaitik Khandelwal, Xiao Liu, Mengmi ZhangNeurIPS
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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. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. 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.