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.

9 papers of 8,653Sort Recent · Most cited
  1. 2023
    InOR-Net: Incremental 3-D Object Recognition Network for Point Cloud RepresentationJiahua Dong, Yang Cong, Gan Sun … Ender KonukoğluTNNLS · Shenyang Institute of Automation · Chinese Academy of Sciences · +4
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  2. 2023
    Uncertainty Estimation With Neural Processes for Meta-Continual LearningXuesong Wang, Lina Yao, Xianzhi Wang … Sen WangTNNLS · University of Technology Sydney · UNSW Sydney · +4
  3. 2023
    Incremental Cluster Validity Index-Guided Online Learning for Performance and Robustness to Presentation OrderLeonardo Enzo Brito da Silva, Nagasharath Rayapati, Donald C. WunschTNNLS · Missouri University of Science and Technology
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  4. 2023
    Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge DistillationTiantian Zhang, Xueqian Wang, Bin Liang, Bo YuanTNNLS · University Town of Shenzhen · Tsinghua University
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  5. 2023
    Unsupervised Continual Learning in Streaming EnvironmentsAndri Ashfahani, Mahardhika PratamaTNNLS · Nanyang Technological University · University of South Australia
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  6. 2023
    KNNENS: A k-Nearest Neighbor Ensemble-Based Method for Incremental Learning Under Data Stream With Emerging New ClassesJianjun Zhang, Ting Wang, Wing W. Y. Ng, Witold PedryczTNNLS · South China University of Technology · Guangzhou First People's Hospital · +2
  7. 2023
    Model Behavior Preserving for Class-Incremental LearningYu Liu, Xiaopeng Hong, Xiaoyu Tao … Yihong GongTNNLS · Xi'an Jiaotong University
  8. 2023
    GopGAN: Gradients Orthogonal Projection Generative Adversarial Network With Continual LearningXiaobin Li, Weiqiang WangTNNLS · University of Chinese Academy of Sciences
  9. 2023
    Lifelong Mixture of Variational AutoencodersFei Ye, Adrian G. BorşTNNLS · University of York
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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.