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Deep neural networks have shown impressive performance in supervised learning, enabled by their ability to fit well to the provided training data.
A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations
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Lev M Bregman · 1967
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Deformed exponentials and logarithms in generalized thermostatistics
Jan Naudts · 2002
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α \alpha -divergence is unique, belonging to both f f -divergence and Bregman divergence classes
Shun-Ichi Amari · 2009
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Variational analysis
R Tyrrell Rockafellar and Roger J-B Wets · 2009
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Families of alpha- beta- and gamma- divergences: Flexible and robust measures of similarities
Andrzej Cichocki and Shun ichi Amari · 2010
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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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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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Auxiliary image regularization for deep cnns with noisy labels
Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, and Trevor Darrell · 2016
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CVXPY: A Python-embedded modeling language for convex optimization
Steven Diamond and Stephen Boyd · 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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f f -divergence inequalities
Igal Sason and Sergio Verdu · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Training deep neural-networks using a noise adaptatio layer
Jacob Goldberger and Ehud Ben-Reuven · 2017
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Distributionally robust stochastic programming
Alexander Shapiro · 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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A rewriting system for convex optimization problems
Akshay Agrawal, Robin Verschueren, Steven Diamond, and Stephen Boyd · 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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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
Large-scale methods for distributionally robust optimization
Daniel Levy, Yair Carmon, John C Duchi, and Aaron Sidford · 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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Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks
Mingchen Li, Mahdi Soltanolkotabi, and Samet Oymak · 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?
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Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
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https://github.com/PaulAlbert31/LabelNoiseCorrection/blob/master/train.py#L196-L197 , 2019
Authors. Official implementation for “Unsupervised Label Noise Modeling and Loss Correction, ICML 2019” · 2019
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Robust bi-tempered logistic loss based on Bregman divergences
Ehsan Amid, Manfred K. K 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 O’Connor, and Kevin McGuinness · 2019
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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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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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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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Extended T: learning with mixed closed-set and open-set noisy labels
Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, and Yinian Mao · 2020
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Multiplicative reweighting for robust neural network optimization
Noga Bar, Tomer Koren, and Raja Giryes · 2021
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Correlated input-dependent label noise in large-scale image classification, 2021
Mark Collier, Basil Mustafa, Efi Kokiopoulou, Rodolphe Jenatton, and Jesse Berent · 2021
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Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2021
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A survey of label-noise representation learning: Past, present and future, 2021
Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, and Masashi Sugiyama · 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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Robust early-learning: Hindering the memorization of noisy labels
Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang · 2021
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