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Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training.
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Class proportion estimation with application to multiclass anomaly rejection
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A. Menon, B. Van Rooyen, C. Ong, and B. Williamson · 2015
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Training deep neural networks on noisy labels with bootstrapping
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Temporal ensembling for semi-supervised learning
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Learning with symmetric label noise: The importance of being unhinged
B. Van Rooyen, A. Menon, and B. Williamson · 2015
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Teaching-to-learn and learning-to-teach for multi-label propagation
C. Gong, D. Tao, J. Yang, and W. Liu · 2016
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Deep Learning
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T. Liu and D. Tao · 2016
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Y. Fan, F. Tian, T. Qin, J. Bian, and T. Liu · 2018
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
L. Jiang, Z. Zhou, T. Leung, L. Li, and L. Fei-Fei · 2018
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Dimensionality-driven learning with noisy labels
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