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Training deep neural networks (DNNs) in the presence of noisy labels is an important and challenging task.
“Imagenet: A large-scale hierarchical image database,”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei, · 2009
Earlier work this paper cites.
“Deep neural networks for acoustic modeling in speech recognition,”
Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Brian Kingsbury, et al., · 2012
Earlier work this paper cites.
“Imagenet classification with deep convolutional neural networks,”
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, · 2012
Earlier work this paper cites.
“Training convolutional networks with noisy labels,”
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus, · 2014
Earlier work this paper cites.
“Distilling the knowledge in a neural network,”
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean, · 2015
Earlier work this paper cites.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Earlier work this paper cites.
“Training deep neural-networks based on unreliable labels,”
Alan Joseph Bekker and Jacob Goldberger, · 2016
Earlier work this paper cites.
“Rethinking the inception architecture for computer vision,”
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna, · 2016
Earlier work this paper cites.
“Identity mappings in deep residual networks,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
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“Attention is all you need,”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin, · 2017
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“Understanding deep learning requires rethinking generalization,”
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals, · 2017
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“A closer look at memorization in deep networks,”
Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al., · 2017
Cited alongside, same era.
“Making deep neural networks robust to label noise: A loss correction approach,”
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu, · 2017
Cited alongside, same era.
“Dimensionality-driven learning with noisy labels,”
Xingjun Ma, Yisen Wang, Michael E Houle, Shuo Zhou, Sarah M Erfani, Shu-Tao Xia, Sudanthi Wijewickrema, and James Bailey, · 2018
Later among the works it cites.
“Generalized cross entropy loss for training deep neural networks with noisy labels,”
Zhilu Zhang and Mert Sabuncu, · 2018
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“Deep bilevel learning,”
Simon Jenni and Paolo Favaro, · 2018
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“mixup: Beyond empirical risk minimization,”
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz, · 2018
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“Symmetric cross entropy for robust learning with noisy labels,”
Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey, · 2019
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“Learning from noisy labels by regularized estimation of annotator confusion,”
Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C Alexander, and Nathan Silberman, · 2019
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“Training deep neural-networks using a noise adaptation layer,”
Jacob Goldberger and Ehud Ben-Reuven, · 2017
Cited alongside, same era.
“Joint optimization framework for learning with noisy labels,”
Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa, · 2018
Cited alongside, same era.
“Iterative learning with open-set noisy labels,”
Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, and Shu-Tao Xia, · 2018
Cited alongside, same era.
Later among the works it cites.
“When does label smoothing help?,”
Rafael Müller, Simon Kornblith, and Geoffrey Hinton, · 2019
Later among the works it cites.