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Deep active sampling with self-supervised learning

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References

  1. Bengar J Z, van de Weijer J, Twardowski B, Raducanu B. Reducing label effort: self-supervised meets active learning. In: Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. 2021, 1631–1639

  2. He K, Fan H, Wu Y, Xie S, Girshick R. Momentum contrast for unsupervised visual representation learning. In: Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020, 9726–9735

  3. Xiao H, Rasul K, Vollgraf R. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. 2017, arXiv preprint arXiv: 1708.07747

  4. Krizhevsky A, Hinton G. Learning multiple layers of features from tiny images. See Google website, 2009

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Haochen Shi

  2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Haochen Shi & Hui Zhou

Authors
  1. Haochen Shi
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  2. Hui Zhou
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Correspondence to Haochen Shi.

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Shi, H., Zhou, H. Deep active sampling with self-supervised learning. Front. Comput. Sci. 17, 174323 (2023). https://doi.org/10.1007/s11704-022-2189-z

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  • DOI: https://doi.org/10.1007/s11704-022-2189-z

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