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Consistency regularization is a commonly-used technique for semi-supervised and self-supervised learning.
Training deep neural networks on noisy labels with bootstrapping
Reed, S., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., and Rabinovich, A · 2014
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Improved regularization of convolutional neural networks with cutout, 2017
DeVries, T. and Taylor, G. W · 2017
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Robust loss functions under label noise for deep neural networks
Ghosh, A., Kumar, H., and Sastry, P · 2017
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Webvision database: Visual learning and understanding from web data, 2017
Li, W., Wang, L., Li, W., Agustsson, E., and Gool, L. V · 2017
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Learning with confident examples: Rank pruning for robust classification with noisy labels
Northcutt, C. G., Wu, T., and Chuang, I. L · 2017
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Making deep neural networks robust to label noise: a loss correction approach, 2017
Patrini, G., Rozza, A., Menon, A., Nock, R., and Qu, L · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A. and Valpola, H · 2017
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Toward robustness against label noise in training deep discriminative neural networks
Vahdat, A · 2017
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Jiang, L., Zhou, Z., Leung, T., Li, J., and Li, F.-F · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Miyato, T., Maeda, S.-i., Koyama, M., and Ishii, S · 2018
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Realistic evaluation of deep semi-supervised learning algorithms
Oliver, A., Odena, A., Raffel, C., Cubuk, E. D., and Goodfellow, I. J · 2018
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Joint optimization framework for learning with noisy labels
Tanaka, D., Ikami, D., Yamasaki, T., and Aizawa, K · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z. and Sabuncu, M · 2018
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Mixmatch: A holistic approach to semi-supervised learning
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., and Raffel, C. A · 2019
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Randaugment: Practical automated data augmentation with a reduced search space, 2019
Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q. V · 2019
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Beyer, L., Hénaff, O. J., Kolesnikov, A., Zhai, X., and Oord, A. v. d · 2020
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Augmix: A simple data processing method to improve robustness and uncertainty
Hendrycks, D., Mu, N., Cubuk, E. D., Zoph, B., Gilmer, J., and Lakshminarayanan, B · 2020
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Graph convolutional networks for learning with few clean and many noisy labels
Iscen, A., Tolias, G., Avrithis, Y., Chum, O., and Schmid, C · 2020
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Early-learning regularization prevents memorization of noisy labels, 2020
Liu, S., Niles-Weed, J., Razavian, N., and Fernandez-Granda, C · 2020
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Peer loss functions: Learning from noisy labels without knowing noise rates
Liu, Y. and Guo, H · 2020
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Li, J., Wong, Y., Zhao, Q., and Kankanhalli, M · 2019
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Self: Learning to filter noisy labels with self-ensembling
Nguyen, D. T., Mummadi, C. K., Ngo, T. P. N., Nguyen, T. H. P., Beggel, L., and Brox, T · 2019
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Combinatorial inference against label noise
Seo, P. H., Kim, G., and Han, B · 2019
Cited alongside, same era.
Symmetric cross entropy for robust learning with noisy labels
Wang, Y., Ma, X., Chen, Z., Luo, Y., Yi, J., and Bailey, J · 2019
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L_dmi: A novel information-theoretic loss function for training deep nets robust to label noise
Xu, Y., Cao, P., Kong, Y., and Wang, Y · 2019
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Dividemix: Learning with noisy labels as semi-supervised learning
Li, J., Socher, R., and Hoi, S. C
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Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks
Li, M., Soltanolkotabi, M., and Oymak, S
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Lukasik, M., Bhojanapalli, S., Menon, A. K., and Kumar, S · 2020
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Normalized loss functions for deep learning with noisy labels, 2020
Ma, X., Huang, H., Wang, Y., Romano, S., Erfani, S., and Bailey, J · 2020
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Generalized jensen-shannon divergence loss for learning with noisy labels, 2021
Englesson, E. and Azizpour, H · 2021
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When optimizing f-divergence is robust with label noise
Wei, J. and Liu, Y · 2021
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Contrast to divide: Self-supervised pre-training for learning with noisy labels, 2021
Zheltonozhskii, E., Baskin, C., Mendelson, A., Bronstein, A. M., and Litany, O · 2021
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