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The noise transition matrix plays a central role in the problem of learning with noisy labels.
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Making deep neural networks robust to label noise: A loss correction approach
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Learning with confident examples: Rank pruning for robust classification with noisy labels
Curtis G Northcutt, Tailin Wu, and Isaac L Chuang · 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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Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
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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
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Fair classification with group-dependent label noise
Jialu Wang, Yang Liu, and Caleb Levy · 2021
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A good representation detects noisy labels
Zhaowei Zhu, Zihao Dong, Hao Cheng, and Yang Liu · 2021
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Clusterability as an alternative to anchor points when learning with noisy labels
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Representation learning with contrastive predictive coding
Aaron Van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Are anchor points really indispensable in label-noise learning?
Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, and Masashi Sugiyama · 2019
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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 · 2019
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Sigua: Forgetting may make learning with noisy labels more robust
Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, and Masashi Sugiyama · 2020
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Dual t: Reducing estimation error for transition matrix in label-noise learning
Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, and Masashi Sugiyama · 2020
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Zhaowei Zhu, Yiwen Song, and Yang Liu · 2021
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Confident learning: Estimating uncertainty in dataset labels
Curtis Northcutt, Lu Jiang, and Isaac Chuang · 2021
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Learning noise transition matrix from only noisy labels via total variation regularization
Yivan Zhang, Gang Niu, and Masashi Sugiyama · 2021
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Provably end-to-end label-noise learning without anchor points
Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, and Masashi Sugiyama · 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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Instance-dependent label-noise learning under a structural causal model
Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, and Kun Zhang · 2021
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Estimating instance-dependent label-noise transition matrix using dnns
Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, and Tongliang 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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Confidence scores make instance-dependent label-noise learning possible
Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, and Masashi Sugiyama · 2021
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Understanding instance-level label noise: Disparate impacts and treatments, 2021
Yang Liu · 2021
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Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, and Bo Han · 2021
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Demystifying how self-supervised features improve training from noisy labels
Hao Cheng, Zhaowei Zhu, Xing Sun, and Yang Liu · 2021
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Contrastive learning improves model robustness under label noise
Aritra Ghosh and Andrew Lan · 2021
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Self-supervised learning disentangled group representation as feature
Tan Wang, Zhongqi Yue, Jianqiang Huang, Qianru Sun, and Hanwang Zhang · 2021
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Contrast to divide: Self-supervised pre-training for learning with noisy labels
Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M Bronstein, and Or Litany · 2022
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