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Noisy labels are ubiquitous in real-world datasets, which poses a challenge for robustly training deep neural networks (DNNs) since DNNs can easily overfit to the noisy labels.
Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O. (2017) · 1906
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Gaussian processes for machine learning
Williams, C. K. and Rasmussen, C. E. (2006) · 2006
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G. (2009) · 2009
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Harvesting image databases from the web
Schroff, F., Criminisi, A., and Zisserman, A. (2011) · 2011
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Imagenet classification with deep convolutional neural networks
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He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality
Amsaleg, L., Bailey, J., Barbe, D., Erfani, S., Houle, M. E., Nguyen, V., and Radovanović, M. (2017) · 2017
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A closer look at memorization in deep networks
Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et al. (2017) · 2017
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Patrini, G., Rozza, A., Menon, A. K., Nock, R., and Qu, L. (2017) · 2017
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Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., and Sugiyama, M. (2018) · 2018
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Jiang, L., Zhou, Z., Leung, T., Li, L.-J., and Fei-Fei, L. (2018) · 2018
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Dimensionality-driven learning with noisy labels
Ma, X., Wang, Y., Houle, M. E., Zhou, S., Erfani, S. M., Xia, S.-T., Wijewickrema, S., and Bailey, J. (2018) · 2018
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Ren, M., Zeng, W., Yang, B., and Urtasun, R. (2018) · 2018
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Joint optimization framework for learning with noisy labels
Tanaka, D., Ikami, D., Yamasaki, T., and Aizawa, K. (2018) · 2018
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Understanding and utilizing deep neural networks trained with noisy labels
Chen, P., Liao, B. B., Chen, G., and Zhang, S. (2019) · 2019
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