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The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e.g., via crowdsourcing).
A lower bound for the smallest singular value of a matrix
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Alexander Philip Dawid and Allan M Skene · 1979
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Inferring ground truth from subjective labelling of venus images
Padhraic Smyth, Usama Fayyad, Michael Burl, Pietro Perona, and Pierre Baldi · 1994
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The rise of crowdsourcing
Jeff Howe et al · 2006
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Lower bounds for the empirical minimization algorithm
Shahar Mendelson · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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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 Movellan, and Paul Ruvolo · 2009
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Sharper lower bounds on the performance of the empirical risk minimization algorithm
Guillaume Lecué and Shahar Mendelson · 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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Towards an integrated crowdsourcing definition
Enrique Estellés-Arolas and Fernando González-Ladrón-de Guevara · 2012
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Ensemble methods: foundations and algorithms
Zhi-Hua Zhou · 2012
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Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
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An evaluation of aggregation techniques in crowdsourcing
Nguyen Quoc Viet Hung, Nguyen Thanh Tam, Lam Ngoc Tran, and Karl Aberer · 2013
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Repeated labeling using multiple noisy labelers
Panagiotis G Ipeirotis, Foster Provost, Victor S Sheng, and Jing Wang · 2014
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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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Gaussian process classification and active learning with multiple annotators
Filipe Rodrigues, Francisco Pereira, and Bernardete Ribeiro · 2014
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An online learning approach to improving the quality of crowd-sourcing
Yang Liu and Mingyan Liu · 2015
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Credbank: A large-scale social media corpus with associated credibility annotations
Tanushree Mitra and Eric Gilbert · 2015
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Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images
Shadi Albarqouni, Christoph Baur, Felix Achilles, Vasileios Belagiannis, Stefanie Demirci, and Nassir Navab · 2016
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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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Active bias: Training more accurate neural networks by emphasizing high variance samples
Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 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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Learning supervised topic models for classification and regression from crowds
Filipe Rodrigues, Mariana Lourenco, Bernardete Ribeiro, and Francisco C Pereira · 2017
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Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge
Arnaud Arindra Adiyoso Setio, Alberto Traverso, Thomas De Bel, Moira SN Berens, Cas Van Den Bogaard, Piergiorgio Cerello, Hao Chen, Qi Dou, Maria Evelina Fantacci, Bram Geurts, et al · 2017
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Majority voting and pairing with multiple noisy labeling
Victor S Sheng, Jing Zhang, Bin Gu, and Xindong Wu · 2017
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Who said what: Modeling individual labelers improves classification
Melody Guan, Varun Gulshan, Andrew Dai, and Geoffrey Hinton · 2018
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When optimizing f f -divergence is robust with label noise
Jiaheng Wei and Yang Liu · 2020
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Robust early-learning: Hindering the memorization of noisy labels
Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang · 2020
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Part-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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Multiplicative reweighting for robust neural network optimization
Noga Bar, Tomer Koren, and Raja Giryes · 2021
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Learning with instance-dependent label noise: A sample sieve approach
Hao Cheng, Zhaowei Zhu, Xingyu Li, Yifei Gong, Xing Sun, and Yang Liu · 2021
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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 Tsang, and Masashi Sugiyama · 2018
Cited alongside, same era.
Deep learning from crowds
Filipe Rodrigues and Francisco Pereira · 2018
Cited alongside, same era.
Robust bi-tempered logistic loss based on Bregman divergences
Ehsan Amid, Manfred K Warmuth, Rohan Anil, and Tomer Koren · 2019
Cited alongside, same era.
Tianyi Luo and Yang Liu · 2019
Cited alongside, same era.
Fighting misinformation on social media using crowdsourced judgments of news source quality
Gordon Pennycook and David G Rand · 2019
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Human uncertainty makes classification more robust
Joshua C Peterson, Ruairidh M Battleday, Thomas L Griffiths, and Olga Russakovsky · 2019
Cited alongside, same era.
Symmetric cross entropy for robust learning with noisy labels
Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey · 2019
Cited alongside, same era.
Demystifying how self-supervised features improve training from noisy labels
Hao Cheng, Zhaowei Zhu, Xing Sun, and Yang Liu · 2021
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Constrained instance and class reweighting for robust learning under label noise
Abhishek Kumar and Ehsan Amid · 2021
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Adaptive early-learning correction for segmentation from noisy annotations
Sheng Liu, Kangning Liu, Weicheng Zhu, Yiqiu Shen, and Carlos Fernandez-Granda · 2021
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Understanding instance-level label noise: Disparate impacts and treatments
Yang Liu · 2021
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Exponentiated gradient reweighting for robust training under label noise and beyond
Negin Majidi, Ehsan Amid, Hossein Talebi, and Manfred K. Warmuth · 2021
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Policy learning using weak supervision
Jingkang Wang, Hongyi Guo, Zhaowei Zhu, and Yang Liu · 2021
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Open-set label noise can improve robustness against inherent label noise
Hongxin Wei, Lue Tao, Renchunzi Xie, and Bo An · 2021
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Understanding generalized label smoothing when learning with noisy labels
Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, and Yang Liu · 2021
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Learning with noisy labels revisited: A study using real-world human annotations
Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, and Yang Liu · 2021
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A second-order approach to learning with instance-dependent label noise
Zhaowei Zhu, Tongliang Liu, and Yang Liu · 2021
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The rich get richer: Disparate impact of semi-supervised learning
Zhaowei Zhu, Tianyi Luo, and Yang Liu · 2021
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Clusterability as an alternative to anchor points when learning with noisy labels
Zhaowei Zhu, Yiwen Song, and Yang Liu · 2021
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Robust training under label noise by over-parameterization
Sheng Liu, Zhihui Zhu, Qing Qu, and Chong You · 2022
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Learning from noisy labels with deep neural networks: A survey
Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee · 2022
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Deep learning from multiple noisy annotators as a union
Hongxin Wei, Renchunzi Xie, Lei Feng, Bo Han, and Bo An · 2022
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Detecting corrupted labels without training a model to predict
Zhaowei Zhu, Zihao Dong, and Yang Liu · 2022
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Beyond images: Label noise transition matrix estimation for tasks with lower-quality features
Zhaowei Zhu, Jialu Wang, and Yang Liu · 2022
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