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The label noise transition matrix $T$, reflecting the probabilities that true labels flip into noisy ones, is of vital importance to model label noise and design statistically consistent classifiers.
On spectral clustering: Analysis and an algorithm
Andrew Y Ng, Michael I Jordan, and Yair Weiss · 2002
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A flexible sigmoid function of determinate growth
Xinyou Yin, JAN Goudriaan, Egbert A Lantinga, JAN Vos, and Huub J Spiertz · 2003
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Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
Gary B Huang, Marwan Mattar, Tamara Berg, and Eric Learned-Miller · 2008
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A clustering method to identify representative financial ratios
Yu-Jie Wang and Hsuan-Shih Lee · 2008
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Covariate shift by kernel mean matching
Arthur Gretton, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt, and Bernhard Schölkopf · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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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 · 2010
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Data clustering: 50 years beyond k-means
Anil K Jain · 2010
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Independent samples t test
Philip Sedgwick · 2010
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Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments
Tianyue Zheng, Weihong Deng, and Jiani Hu · 2010
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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A survey of label-noise representation learning: Past, present and future
Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W Tsang, James T Kwok, and Masashi Sugiyama · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y.Ng · 2011
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Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
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A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
Clayton Scott · 2015
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Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Yandong Guo, Lei Zhang, Yuxiao Hu, Xiaodong He, and Jianfeng Gao · 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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Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 2016
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Frontal to profile face verification in the wild
Soumyadip Sengupta, Jun-Cheng Chen, Carlos Castillo, Vishal M Patel, Rama Chellappa, and David W Jacobs · 2016
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Joint unsupervised learning of deep representations and image clusters
Jianwei Yang, Devi Parikh, and Dhruv Batra · 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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Agedb: the first manually collected, in-the-wild age database
Stylianos Moschoglou, Athanasios Papaioannou, Christos Sagonas, Jiankang Deng, Irene Kotsia, and Stefanos Zafeiriou · 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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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 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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Vggface2: A dataset for recognising faces across pose and age
L_dmi: A novel information-theoretic loss function for training deep nets robust to label noise
Yilun Xu, Peng Cao, Yuqing Kong, and Yizhou Wang · 2019
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Deep spectral clustering using dual autoencoder network
Xu Yang, Cheng Deng, Feng Zheng, Junchi Yan, and Wei Liu · 2019
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How does disagreement benefit co-teaching?
Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W Tsang, and Masashi Sugiyama · 2019
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Confidence scores make instance-dependent label-noise learning possible
Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, and Masashi Sugiyama · 2020
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Rethinking importance weighting for deep learning under distribution shift
Tongtong Fang, Nan Lu, Gang Niu, and Masashi Sugiyama · 2020
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Qiong Cao, Li Shen, Weidi Xie, Omkar M Parkhi, and Andrew Zisserman · 2018
Cited alongside, same era.
Mobilefacenets: Efficient cnns for accurate real-time face verification on mobile devices
Sheng Chen, Yang Liu, Xiang Gao, and Zhen Han · 2018
Cited alongside, same era.
Curriculumnet: Weakly supervised learning from large-scale web images
Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R Scott, and Dinglong Huang · 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
Cited alongside, same era.
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
Cited alongside, same era.
Discriminatively boosted image clustering with fully convolutional auto-encoders
Fengfu Li, Hong Qiao, and Bo Zhang · 2018
Cited alongside, same era.
Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
Cited alongside, same era.
Lu Jiang, Mason Liu Di Huang, and Weilong Yang · 2020
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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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Early-learning regularization prevents memorization of noisy labels
Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda · 2020
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Peer loss functions: Learning from noisy labels without knowing noise rates
Yang Liu and Hongyi Guo · 2020
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Does label smoothing mitigate label noise?
Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, and Sanjiv Kumar · 2020
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Normalized loss functions for deep learning with noisy labels
Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, and James Bailey · 2020
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Can gradient clipping mitigate label noise?
Aditya Krishna Menon, Ankit Singh Rawat, Sashank J Reddi, and Sanjiv Kumar · 2020
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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 · 2020
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Evidentialmix: Learning with combined open-set and closed-set noisy labels
Ragav Sachdeva, Filipe R Cordeiro, Vasileios Belagiannis, Ian Reid, and Gustavo Carneiro · 2020
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Meta transition adaptation for robust deep learning with noisy labels
Jun Shu, Qian Zhao, Zengben Xu, and Deyu Meng · 2020
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Fair classification with group-dependent label noise
Jialu Wang, Yang Liu, and Caleb Levy · 2020
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Combating noisy labels by agreement: A joint training method with co-regularization
Hongxin Wei, Lei Feng, Xiangyu Chen, and Bo An · 2020
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When optimizing f f -divergence is robust with label noise
Jiaheng Wei and Yang Liu · 2020
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Class2simi: A new perspective on learning with label noise
Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, and Gang Niu · 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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Error-bounded correction of noisy labels
Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas, and Chao Chen · 2020
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