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It is important to learn various types of classifiers given training data with noisy labels.
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Learning with noisy labels
N. Natarajan, I. Dhillon, P. Ravikumar, and A. Tewari · 2013
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Generative adversarial nets
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Learning from multiple annotators with varying expertise
Y. Yan, R. Rosales, G. Fung, R. Subramanian, and J. Dy · 2014
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Learning from corrupted binary labels via class-probability estimation
A. Menon, B. Van Rooyen, C. Ong, and B. Williamson · 2015
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Training deep neural networks on noisy labels with bootstrapping
S. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan, and A. Rabinovich · 2015
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Training convolutional networks with noisy labels
S. Sukhbaatar, J. Bruna, M. Paluri, L. Bourdev, and R. Fergus · 2015
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Learning from massive noisy labeled data for image classification
T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang · 2015
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Auxiliary image regularization for deep cnns with noisy labels
S. Azadi, J. Feng, S. Jegelka, and T. Darrell · 2016
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E. Malach and S. Shalev-Shwartz · 2017
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Making deep neural networks robust to label noise: A loss correction approach
G. Patrini, A. Rozza, A. Menon, R. Nock, and L. Qu · 2017
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Hierarchical implicit models and likelihood-free variational inference
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Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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On the convergence of a family of robust losses for stochastic gradient descent
B. Han, I. Tsang, and L. Chen · 2016
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Classification with noisy labels by importance reweighting
T. Liu and D. Tao · 2016
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Virtual adversarial training: A regularization method for supervised and semi-supervised learning
T. Miyato, S. Maeda, M. Koyama, and S. Ishii · 2016
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A closer look at memorization in deep networks
D. Arpit, S. Jastrzębski, N. Ballas, D. Krueger, E. Bengio, M. Kanwal, T. Maharaj, A. Fischer, A. Courville, and Y. Bengio · 2017
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Training deep neural-networks using a noise adaptation layer
J. Goldberger and E. Ben-Reuven · 2017
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A. Veit, N. Alldrin, G. Chechik, I. Krasin, A. Gupta, and S. Belongie · 2017
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Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2017
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Training a neural network based on unreliable human annotation of medical images
Y. Dgani, H. Greenspan, and J. Goldberger · 2018
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Using trusted data to train deep networks on labels corrupted by severe noise
D. Hendrycks, M. Mazeika, D. Wilson, and K. Gimpel · 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
X. Ma, Y. Wang, M. Houle, S. Zhou, S. Erfani, S. Xia, S. Wijewickrema, and J. Bailey · 2018
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Learning to reweight examples for robust deep learning
M. Ren, W. Zeng, B. Yang, and R. Urtasun · 2018
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Deep learning from crowds
F. Rodrigues and F. Pereira · 2018
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Joint optimization framework for learning with noisy labels
D. Tanaka, D. Ikami, T. Yamasaki, and K. Aizawa · 2018
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Iterative learning with open-set noisy labels
Y. Wang, W. Liu, X. Ma, J. Bailey, H. Zha, L. Song, and S. Xia · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Z. Zhang and M. Sabuncu · 2018
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