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1 Algan, G. and Ulusoy, I. (2020). Label noise types and their effects on deep learning. arXiv preprint arXiv:2003.10471
2 Frénay, B. and Verleysen, M. (2013). Classification in the presence of label noise: a survey. IEEE transactions on neural networks and learning systems, 25(5):845–869
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7 Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., and Qu, L. (2017). Making deep neural networks robust to label noise: A loss correction approach. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1944–1952
8 Rolnick, D., Veit, A., Belongie, S., and Shavit, N. (2017). Deep learning is robust to massive label noise. arXiv preprint arXiv:1705.10694
9 Rusiecki, A. (2020). Standard dropout as remedy for training deep neural networks with label noise. In International Conference on Dependability and Complex Systems, pages 534–542. Springer
10 Russell, S. J. and Norvig, P. (2021). Artificial Intelligence: A Modern Approach. Pearson, global edition
11 Simard, P. Y., Steinkraus, D., Platt, J. C., et al. (2003). Best practices for convolutional neural networks applied to visual document analysis. In Icdar, volume 3
12 Song, H., Kim, M., Park, D., Shin, Y., and Lee, J.-G. (2020). Learning from noisy labels with deep neural networks: A survey. arXiv preprint arXiv:2007.08199
13 Xiao, H., Rasul, K., and Vollgraf, R. (2017). Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747
14 Zhang, Z. and Sabuncu, M. (2018). Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in neural information processing systems, 31