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Deep learning requires data.
Training connectionist networks with queries and selective sampling
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An empirical comparison of three boosting algorithms on real data sets with artificial class noise
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Semi-supervised learning by entropy minimization
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Semi-supervised learning literature survey
Xiaojin Zhu · 2005
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Entropy regularization
Yves Grandvalet and Yoshua Bengio · 2006
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Two-view feature generation model for semi-supervised learning
Rie Kubota Ando and Tong Zhang · 2007
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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
Gary B Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller · 2007
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The balanced accuracy and its posterior distribution
Kay Henning Brodersen, Cheng Soon Ong, Klaas Enno Stephan, and Joachim M Buhmann · 2010
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Quality management on amazon mechanical turk
Panagiotis G Ipeirotis, Foster Provost, and Jing Wang · 2010
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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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Training convolutional networks with noisy labels
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Probabilistic learning from mislabelled data for multimedia content recognition
Pravin Kakar and Alex Yong-Sang Chia · 2015
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Age and gender classification using convolutional neural networks
Gil Levi and Tal Hassner · 2015
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Evaluation of face recognition apis and libraries
Philip Masek and Magnus Thulin · 2015
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Active learning literature survey
Burr Settles · 2010
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Label-noise robust logistic regression and its applications
Jakramate Bootkrajang and Ata Kabán · 2012
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Learning to label aerial images from noisy data
Volodymyr Mnih and Geoffrey E Hinton · 2012
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Boosting in the presence of label noise
Jakramate Bootkrajang and Ata Kabán · 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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The power of localization for efficiently learning linear separators with noise
Pranjal Awasthi, Maria Florina Balcan, and Philip M Long · 2014
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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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Training deep neural-networks based on unreliable labels
Alan Joseph Bekker and Jacob Goldberger · 2016
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Learning from binary labels with instance-dependent corruption
Aditya Krishna Menon, Brendan van Rooyen, and Nagarajan Natarajan · 2016
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Loss factorization, weakly supervised learning and label noise robustness
Giorgio Patrini, Frank Nielsen, Richard Nock, and Marcello Carioni · 2016
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Making neural networks robust to label noise: a loss correction approach
Giorgio Patrini, Alessandro Rozza, Aditya Menon, Richard Nock, and Lizhen Qu · 2016
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Attend in groups: a weakly-supervised deep learning framework for learning from web data
Bohan Zhuang, Lingqiao Liu, Yao Li, Chunhua Shen, and Ian Reid · 2016
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On the robustness of convnets to training on noisy labels
David Flatow and Daniel Penner · 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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