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It has been hypothesized that label smoothing can reduce overfitting and improve generalization, and current empirical evidence seems to corroborate these effects.
Efficient learning of naive bayes classifiers under class-conditional classification noise
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Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
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Classification with asymmetric label noise: Consistency and maximal denoising
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Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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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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Ziyin Liu, Zhikang Wang, Paul Pu Liang, Russ R Salakhutdinov, Louis-Philippe Morency, and Masahito Ueda · 2019
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
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Label smoothing and logit squeezing: A replacement for adversarial training?
Ali Shafahi, Amin Ghiasi, Furong Huang, and Tom Goldstein · 2019
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Sanjeev Arora, Rong Ge, Behnam Neyshabur, and Yi Zhang · 2018
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Gradient descent provably optimizes over-parameterized neural networks
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Safeguarded dynamic label regression for noisy supervision
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Does label smoothing mitigate label noise?, 2020
Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, and Sanjiv Kumar · 2020
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Generalized entropy regularization or: There’s nothing special about label smoothing
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Learning not to learn in the presence of noisy labels
Liu Ziyin, Blair Chen, Ru Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, and Masahito Ueda · 2020
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A simple approach to the noisy label problem through the gambler’s loss, 2020
Liu Ziyin, Ru Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, and Masahito Ueda · 2020
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