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The transition matrix, denoting the transition relationship from clean labels to noisy labels, is essential to build statistically consistent classifiers in label-noise learning.
Information theory and statistics: A tutorial
Imre Csiszár, Paul C Shields, et al · 2004
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Learning classifiers from only positive and unlabeled data
Charles Elkan and Keith Noto · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Concentration inequalities: A nonasymptotic theory of independence
Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013
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Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
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Classification with asymmetric label noise: Consistency and maximal denoising
Clayton Scott, Gilles Blanchard, and Gregory Handy · 2013
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Training deep neural networks on noisy labels with bootstrapping
Scott E Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2014
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A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
Clayton Scott · 2015
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Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 2016
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Mixture proportion estimation via kernel embeddings of distributions
Harish Ramaswamy, Clayton Scott, and Ambuj Tewari · 2016
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A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
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Training deep neural-networks using a noise adaptation layer
Jacob Goldberger and Ehud Ben-Reuven · 2017
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Learning from noisy labels with distillation
Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, and Li-Jia Li · 2017
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Decoupling" when to update" from" how to update"
Eran Malach and Shai Shalev-Shwartz · 2017
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Learning with confident examples: Rank pruning for robust classification with noisy labels
Curtis G Northcutt, Tailin Wu, and Isaac L Chuang · 2017
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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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Toward robustness against label noise in training deep discriminative neural networks
Arash Vahdat · 2017
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Learning from noisy large-scale datasets with minimal supervision
Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, and Serge Belongie · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Robustness of conditional gans to noisy labels
Kiran K Thekumparampil, Ashish Khetan, Zinan Lin, and Sewoong Oh · 2018
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Learning with biased complementary labels
Xiyu Yu, Tongliang Liu, Mingming Gong, and Dacheng Tao · 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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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert Sabuncu · 2018
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Generalization bounds for neural networks via approximate description length
Amit Daniely and Elad Granot · 2019
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Peer loss functions: Learning from noisy labels without knowing noise rates
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Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Transfer learning with label noise
Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, and Dacheng Tao · 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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Classification from pairwise similarity and unlabeled data
Han Bao, Gang Niu, and Masashi Sugiyama · 2018
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Curriculumnet: Weakly supervised learning from large-scale web images
Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R Scott, and Dinglong Huang · 2018
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MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
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Robust active label correction
Jan Kremer, Fei Sha, and Christian Igel · 2018
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Yang Liu and Hongyi Guo · 2019
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Are anchor points really indispensable in label-noise learning?
Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, and Masashi Sugiyama · 2019
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L_dmi: A novel information-theoretic loss function for training deep nets robust to label noise
Yilun Xu, Peng Cao, Yuqing Kong, and Yizhou Wang · 2019
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How does disagreement help generalization against label corruption?
Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W Tsang, and Masashi Sugiyama · 2019
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Learning with bounded instance-and label-dependent label noise
Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, and Dacheng Tao · 2020
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Sigua: Forgetting may make learning with noisy labels more robust
Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor W Tsang, and Masashi Sugiyama · 2020
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Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks
Mingchen Li, Mahdi Soltanolkotabi, and Samet Oymak · 2020
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Class2simi: A new perspective on learning with label noise
Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, and Gang Niu · 2020
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Parts-dependent label noise: Towards instance-dependent label noise
Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, and Masashi Sugiyama · 2020
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Searching to exploit memorization effect in learning with noisy labels
Quanming Yao, Hansi Yang, Bo Han, Gang Niu, and J Kwok · 2020
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