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Deep learning has achieved excellent performance in various computer vision tasks, but requires a lot of training examples with clean labels.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert R. Sabuncu · 1903
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Induction of decision trees
J. Ross Quinlan · 1986
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Learning from noisy examples
Dana Angluin and Philip D. Laird · 1988
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Discovering informative patterns and data cleaning
Isabelle Guyon, Nada Matic, and Vladimir Vapnik · 1996
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Design of robust neural network classifiers
Jan Larsen, Lars Nonboe Andersen, Mads Hintz-Madsen, and Lars Kai Hansen · 1998
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Identifying mislabeled training data
Carla E. Brodley and Mark A. Friedl · 1999
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
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Alex Krizhevsky · 2009
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Learning object categories from Internet image searches
Robert Fergus, Fei-Fei Li, Pietro Perona, and Andrew Zisserman · 2010
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P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
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Harvesting image databases from the web
Florian Schroff, Antonio Criminisi, and Andrew Zisserman · 2011
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Classification in the presence of label noise: A survey
Benoît Frénay and Michel Verleysen · 2014
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Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2014
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Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2015
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Michael J. Wilber, Iljung S. Kwak, David J. Kriegman, and Serge J. Belongie · 2015
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Learning from massive noisy labeled data for image classification
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Deep residual learning for image recognition
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
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Deep learning is robust to massive label noise
David Rolnick, Andreas Veit, Serge Belongie, and Nir Shavit · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Toward robustness against label noise in training deep discriminative neural networks
Arash Vahdat · 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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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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The unreasonable effectiveness of noisy data for fine-grained recognition
Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, and Li Fei-Fei · 2016
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Deep label distribution learning with label ambiguity
Bin-Bin Gao, Chao Xing, Chen-Wei Xie, Jianxin Wu, and Xin Geng · 2017
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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and P. S. Sastry · 2017
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CleanNet: Transfer learning for scalable image classifier training with label noise
Kuang-Huei Lee, Xiaodong He, Lei Zhang, and Linjun Yang · 2018
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Dimensionality-driven learning with noisy labels
Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah M. Erfani, Shu-Tao Xia, Sudanthi N. R. Wijewickrema, and James Bailey · 2018
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Joint optimization framework for learning with noisy labels
Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2018
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Iterative learning with open-set noisy labels
Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, and Shu-Tao Xia · 2018
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Deep learning from noisy image labels with quality embedding
Jiangchao Yao, Jiajie Wang, Ivor W Tsang, Ya Zhang, Jun Sun, Chengqi Zhang, and Rui Zhang · 2018
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