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The presence of mislabeled observations in data is a notoriously challenging problem in statistics and machine learning, associated with poor generalization properties for both traditional classifiers and, perhaps even more so, flexible classifiers like neural networks.
Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
Earlier work this paper cites.
Classification in the presence of label noise: A survey
Benoît Frénay and Michel Verleysen · 2014
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Training deep neural networks on noisy labels with bootstrapping
Scott E. Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna Estrach, Manohar Paluri, Lubomir Bourdev, and Robert Fergus · 2015
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Penalized weighted least squares for outlier detection and robust regression
Xiaoli Gao and Yixin Fang · 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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Classification with noisy labels by importance reweighting
Tonglian Liu and Dacheng Tao · 2016
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Robust High-dimensional Data Analysis Using a Weight Shrinkage Rule
Bin Luo · 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, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
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Penalize weighted least absolute deviation regression
XL Gao and Yang Feng · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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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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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 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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Unsupervised label noise modeling and loss correction
Eric Arazo Sanchez, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness · 2019
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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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David Rolnick, Andreas Veit, Serge Belongie, and Nir Shavit · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
Geoff Pleiss, Tianyi Zhang, Ethan R Elenberg, and Kilian Q Weinberger · 2020
Later among the works it cites.
Learning from noisy labels with deep neural networks: A survey
Hwanjun Song, Minseok Kim, Dongmin Park, and Jae-Gil Lee · 2020
Later among the works it cites.