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Human-annotated labels are often prone to noise, and the presence of such noise will degrade the performance of the resulting deep neural network (DNN) models.
Rademacher and gaussian complexities: Risk bounds and structural results
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Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 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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Learning statistical models of phenotypes using noisy labeled training data
Vibhu Agarwal, Tanya Podchiyska, Juan M Banda, Veena Goel, Tiffany I Leung, Evan P Minty, Timothy E Sweeney, Elsie Gyang, and Nigam H Shah · 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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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
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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 · 2017
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Learning from noisy labels with distillation
Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, and Li-Jia Li · 2017
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Machine-learning aided peer prediction
Yang Liu and Yiling Chen · 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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Improving crowdsourced label quality using noise correction
Jing Zhang, Victor S Sheng, Tao Li, and Xindong Wu · 2017
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Decomposition-based evolutionary multiobjective optimization to self-paced learning
Maoguo Gong, Hao Li, Deyu Meng, Qiguang Miao, and Jia Liu · 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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Joint optimization framework for learning with noisy labels
Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2018
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mixup: Beyond empirical risk minimization
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 W Tsang, and Masashi Sugiyama · 2019
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Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation
Amr M. Alexandari, Anshul Kundaje, and Avanti Shrikumar · 2020
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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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Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert Sabuncu · 2018
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Robust bi-tempered logistic loss based on bregman divergences
Ehsan Amid, Manfred KK Warmuth, Rohan Anil, and Tomer Koren · 2019
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Unsupervised label noise modeling and loss correction
Eric Arazo, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness · 2019
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Deep self-learning from noisy labels
Jiangfan Han, Ping Luo, and Xiaogang Wang · 2019
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Nlnl: Negative learning for noisy labels
Youngdong Kim, Junho Yim, Juseung Yun, and Junmo Kim · 2019
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Learning to learn from noisy labeled data
Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S Kankanhalli · 2019
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Coherent gradients: An approach to understanding generalization in gradient descent-based optimization
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Learning with bounded instance-and label-dependent label noise
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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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Dividemix: Learning with noisy labels as semi-supervised learning
Junnan Li, Richard Socher, and Steven C.H. Hoi · 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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A bi-level formulation for label noise learning with spectral cluster discovery
Yijing Luo, Bo Han, and Chen Gong · 2020
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Learning adaptive loss for robust learning with noisy labels
Jun Shu, Qian Zhao, Keyu Chen, Zongben Xu, and Deyu Meng · 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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Parts-dependent label noise: Towards instance-dependent label noise
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Explaining memorization and generalization: A large-scale study with coherent gradients
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The importance of understanding instance-level noisy labels, 2021
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When optimizing $f$-divergence is robust with label noise
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