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Deep learning with noisy labels is a challenging task.
Gradient-based learning applied to document recognition
Yann Lecun, Leon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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A study of gaussian mixture models of color and texture features for image classification and segmentation
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, and Fei-Fei Li · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Co-regularization based semi-supervised domain adaptation
Abhishek Kumar, Avishek Saha, and Hal Daume · 2010
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Harvesting image databases from the web
Florian Schroff, Antonio Criminisi, and Andrew Zisserman · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Distilling effective supervision from severe label noise
Zizhao Zhang, Han Zhang, Sercan Arik, Honglak Lee, and Tomas Pfister · 2012
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Learning with noisy labels
Nagarajan Natarajan, Inderjit Dhillon, Pradeep Ravikumar, and Ambuj Tewari · 2013
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Learning from corrupted binary labels via class-probability estimation
Aditya Menon, Brendan Van Rooyen, Cheng Soon Ong, and Bob Williamson · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 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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Noise detection in the meta-learning level
Luis Garcia, Andre de Carvalho, and Ana Lorena · 2016
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Training deep neural-networks using a noise adaptation layer
Jacob Goldberger and Ehud Ben-Reuven · 2016
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 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 closer look at memorization in deep networks
Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, and Yoshua Bengio · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
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Decoupling" when to update" from" how to update"
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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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Prototypical networks for few-shot learning
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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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Deep self-learning from noisy labels
Jiangfan Han, Ping Luo, and Xiaogang Wang · 2019
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Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum · 2019
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Learning to learn from noisy labeled data
Junnan Li, Yongkang Wong, Qi Zhao, and Mohan Kankanhalli · 2019
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Meta-weight-net: Learning an explicit mapping for sample weighting
Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng · 2019
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Selfie: Refurbishing unclean samples for robust deep learning
Hwanjun Song, Minseok Kim, and Jae-Gil Lee · 2019
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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A semi-supervised two-stage approach to learning from noisy labels
Yifan Ding, Liqiang Wang, Deliang Fan, and Boqing Gong · 2018
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
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Using trusted data to train deep networks on labels corrupted by severe noise
Dan Hendrycks, Mantas Mazeika, Duncan Wilson, and Kevin Gimpel · 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 Fei-Fei Li · 2018
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Symmetric cross entropy for robust learning with noisy labels
Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey · 2019
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L_dmi: An information-theoretic noise-robust loss function
Yilun Xu, Peng Cao, Yuqing Kong, and Yizhou Wang · 2019
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Probabilistic end-to-end noise correction for learning with noisy labels
Kun Yi and Jianxin Wu · 2019
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How does disagreement help generalization against label corruption?
Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor Tsang, and Masashi Sugiyama · 2019
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Pseudo-labeling and confirmation bias in deep semi-supervised learning
Eric Arazo, Diego Ortego, Paul Albert, Noel O’Connor, and Kevin McGuinness · 2020
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Can cross entropy loss be robust to label noise?
Lei Feng, Senlin Shu, Zhuoyi Lin, Fengmao Lv, Li Li, and Bo An · 2020
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Dividemix: Learning with noisy labels as semi-supervised learning
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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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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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Training noise-robust deep neural networks via meta-learning
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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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