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Deep neural networks are known to be annotation-hungry.
Learning from noisy labels with distillation
Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, and Li-Jia Li · 1936
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Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
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A study of gaussian mixture models of color and texture features for image classification and segmentation
Haim H. Permuter, Joseph M. Francos, and Ian Jermyn · 2006
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 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 · 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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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A closer look at memorization in deep networks
Devansh Arpit, Stanislaw Jastrzkebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, and Simon Lacoste-Julien · 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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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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Decoupling “when to update” from “how to update”
Eran Malach and Shai Shalev-Shwartz · 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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Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, and Geoffrey E. Hinton · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A. Alemi · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 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 J. Belongie · 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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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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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2018
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Unsupervised label noise modeling and loss correction
Eric Arazo, Diego Ortego, Paul Albert, Noel E. O’Connor, and Kevin McGuinness · 2019
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
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Understanding and utilizing deep neural networks trained with noisy labels
Pengfei Chen, Benben Liao, Guangyong Chen, and Shengyu Zhang · 2019
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Recycling: Semi-supervised learning with noisy labels in deep neural networks
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Yifan Ding, Liqiang Wang, Deliang Fan, and Boqing Gong · 2018
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, and Masashi Sugiyama · 2018
Cited alongside, same era.
Using trusted data to train deep networks on labels corrupted by severe noise
Dan Hendrycks, Mantas Mazeika, Duncan Wilson, and Kevin Gimpel · 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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Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Tom Duerig, and Vittorio Ferrari · 2018
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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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Kyeongbo Kong, Junggi Lee, Youngchul Kwak, Minsung Kang, Seong Gyun Kim, and Woo-Jin Song · 2019
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Robust learning from untrusted sources
Nikola Konstantinov and Christoph Lampert · 2019
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Learning to learn from noisy labeled data
Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S. Kankanhalli · 2019
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Virtual adversarial training: A regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2019
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Learning with bad training data via iterative trimmed loss minimization
Yanyao Shen and Sujay Sanghavi · 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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Combating label noise in deep learning using abstention
Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff A. Bilmes, Gopinath Chennupati, and Jamal Mohd-Yusof · 2019
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Probabilistic end-to-end noise correction for learning with noisy labels
Kun Yi and Jianxin Wu · 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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Metacleaner: Learning to hallucinate clean representations for noisy-labeled visual recognition
Weihe Zhang, Yali Wang, and Yu Qiao · 2019
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