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The predictive performance of supervised learning algorithms depends on the quality of labels.
Maximum likelihood estimation of observer error-rates using the em algorithm
Alexander Philip Dawid and Allan M Skene · 1979
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Inferring ground truth from subjective labelling of venus images
Padhraic Smyth, Usama M Fayyad, Michael C Burl, Pietro Perona, and Pierre Baldi · 1995
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Gradient-based learning applied to document recognition
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Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation
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The wisdom of crowds
James Surowiecki · 2005
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Bi-rads lexicon for us and mammography: interobserver variability and positive predictive value
Elizabeth Lazarus, Martha B Mainiero, Barbara Schepps, Susan L Koelliker, and Linda S Livingston · 2006
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Whose vote should count more: Optimal integration of labels from labelers of unknown expertise
Jacob Whitehill, Ting-fan Wu, Jacob Bergsma, Javier R Movellan, and Paul L Ruvolo · 2009
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Supervised learning from multiple experts: whom to trust when everyone lies a bit
Vikas C Raykar, Shipeng Yu, Linda H Zhao, Anna Jerebko, Charles Florin, Gerardo Hermosillo Valadez, Luca Bogoni, and Linda Moy · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Online crowdsourcing: rating annotators and obtaining cost-effective labels
Peter Welinder and Pietro Perona · 2010
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The multidimensional wisdom of crowds
Peter Welinder, Steve Branson, Pietro Perona, and Serge J Belongie · 2010
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Modeling annotator expertise: Learning when everybody knows a bit of something
Yan Yan, Rómer Rosales, Glenn Fung, Mark Schmidt, Gerardo Hermosillo, Luca Bogoni, Linda Moy, and Jennifer Dy · 2010
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Learning from crowds
Vikas C Raykar, Shipeng Yu, Linda H Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, and Linda Moy · 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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Interobserver variability in the ct assessment of honeycombing in the lungs
Takeyuki Watadani, Fumikazu Sakai, Takeshi Johkoh, Satoshi Noma, Masanori Akira, Kiminori Fujimoto, Alexander A Bankier, Kyung Soo Lee, Nestor L Müller, Jae-Woo Song, et al · 2013
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Comparison of interreader reproducibility of the prostate imaging reporting and data system and likert scales for evaluation of multiparametric prostate mri
Andrew B Rosenkrantz, Ruth P Lim, Mershad Haghighi, Molly B Somberg, James S Babb, and Samir S Taneja · 2013
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Active visual recognition with expertise estimation in crowdsourcing
Chengjiang Long, Gang Hua, and Ashish Kapoor · 2013
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Learning from multiple annotators: distinguishing good from random labelers
Filipe Rodrigues, Francisco Pereira, and Bernardete Ribeiro · 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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Lean crowdsourcing: Combining humans and machines in an online system
Steve Branson, Grant Van Horn, and Pietro Perona · 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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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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Training deep neural-networks using a noise adaptation layer
Jacob Goldberger and Ehud Ben-Reuven · 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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Multimodal prediction and personalization of photo edits with deep generative models
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Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 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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Classification in the presence of label noise: a survey
Benoît Frénay and Michel Verleysen · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Multi-class multi-annotator active learning with robust gaussian process for visual recognition
Chengjiang Long and Gang Hua · 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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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2015
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Ardavan Saeedi, Matthew D Hoffman, Stephen J DiVerdi, Asma Ghandeharioun, Matthew J Johnson, and Ryan P Adams · 2017
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Learning deep resnet blocks sequentially using boosting theory
Furong Huang, Jordan T. Ash, John Langford, and Robert E. Schapire · 2017
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Lean multiclass crowdsourcing
Grant Van Horn, Steve Branson, Scott Loarie, Serge Belongie, Cornell Tech, and Pietro Perona · 2018
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Learning from noisy singly-labeled data
Ashish Khetan, Zachary C Lipton, and Anima Anandkumar · 2018
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Who said what: Modeling individual labelers improves classification
Melody Y Guan, Varun Gulshan, Andrew M Dai, and Geoffrey E Hinton · 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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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 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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A probabilistic u-net for segmentation of ambiguous images
Simon AA Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R Ledsam, Klaus H Maier-Hein, SM Eslami, Danilo Jimenez Rezende, and Olaf Ronneberger · 2018
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