Deep active sampling with self-supervised learning

Haochen SHI , Hui ZHOU

Front. Comput. Sci. ›› 2023, Vol. 17 ›› Issue (4) : 174323

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Front. Comput. Sci. ›› 2023, Vol. 17 ›› Issue (4) : 174323 DOI: 10.1007/s11704-022-2189-z
Artificial Intelligence
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Deep active sampling with self-supervised learning

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Haochen SHI, Hui ZHOU. Deep active sampling with self-supervised learning. Front. Comput. Sci., 2023, 17(4): 174323 DOI:10.1007/s11704-022-2189-z

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References

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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

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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

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

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

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