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The efficacy of deep learning depends on large-scale data sets that have been carefully curated with reliable data acquisition and annotation processes.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Loop: local outlier probabilities
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, and Arthur Zimek · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Training Deep Neural Networks on Noisy Labels with Bootstrapping
Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2014
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Deep learning
Yann LeCun, Y. Bengio, and Geoffrey Hinton · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 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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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 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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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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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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Evidential deep learning to quantify classification uncertainty, 2018
Murat Sensoy, Lance Kaplan, and Melih Kandemir · 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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MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
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Nlnl: Negative learning for noisy labels
Youngdong Kim, Junho Yim, Juseung Yun, and Junmo Kim · 2019
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Robust inference via generative classifiers for handling noisy labels
Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, and Jinwoo Shin · 2019
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Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
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Uncertainty Aware AI ML: Why and How
Lance Kaplan, Federico Cerutti, Murat Sensoy, Alun Preece, and Paul Sullivan · 2018
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Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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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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SELF: Learning to Filter Noisy Labels with Self-Ensembling
Duc Tam Nguyen, Chaithanya Kumar Mummadi, Thi Phuong Nhung Ngo, Thi Hoai Phuong Nguyen, Laura Beggel, and Thomas Brox · 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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DivideMix: Learning with Noisy Labels as Semi-supervised Learning
Junnan Li, Richard Socher, and Steven C. H. Hoi · 2020
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