Articles | Volume 13, issue 1
https://doi.org/10.5194/jsss-13-63-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.Special issue:
Cutout as augmentation in contrastive learning for detecting burn marks in plastic granules
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J. Sens. Sens. Syst., 13, 187–209,
2024J. Sens. Sens. Syst., 11, 277–285,
2022J. Sens. Sens. Syst., 10, 153–162,
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2018Cited articles
Caron, M., Bojanowski, P., Joulin, A., and Douze, M.: Deep clustering for unsupervised learning of visual features, in: Proceedings of the European conference on computer vision (ECCV), 8 September 2018, Munich, Germany, Springer, Cham, 132–149, https://doi.org/10.1007/978-3-030-01264-9_9, 2018.
Chandola, V., Banerjee, A., and Kumar, V.: Anomaly detection: A survey[J]. ACM computing surveys (CSUR), https://doi.org/10.1145/1541880.1541882, 2009.
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G.: A simple framework for contrastive learning of visual representations, in: International conference on machine learning, PMLR, 12 July 2020, Vienna, Austria, 1597–1607, arXiv [preprint], https://doi.org/10.48550/arXiv.2011.02578, 4 November 2020.
DeVries, T. and Taylor, G. W.: Improved regularization of convolutional neural networks with cutout, arXiv [preprint], https://doi.org/10.48550/arXiv.1708.04552, 15 August 2017.
Gidaris, S., Singh, P., and Komodakis, N.: Unsupervised representation learning by predicting image rotations, arXiv [preprint], https://doi.org/10.48550/arXiv.1803.07728, 21 March 2018.