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Better-supervised models might have better performance.
Learning from noisy examples
Dana Angluin and Philip Laird · 1988
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Efficient estimations from a slowly convergent Robbins-Monro process
David Ruppert · 1988
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Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 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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Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
Sergey Zagoruyko and Nikos Komodakis · 2017
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Born again neural networks
Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar · 2018
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An embarrassingly simple approach for knowledge distillation
Mengya Gao, Yujun Shen, Quanquan Li, Junjie Yan, Liang Wan, Dahua Lin, Chen Change Loy, and Xiaoou Tang · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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MobileNetV2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, and Ruosong Wang · 2019
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On the efficacy of knowledge distillation
Jang Hyun Cho and Bharath Hariharan · 2019
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Understanding knowledge distillation
Towards understanding ensemble, knowledge distillation and self-distillation in deep learning
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Self-adaptive training: beyond empirical risk minimization
Lang Huang, Chao Zhang, and Hongyang Zhang · 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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Understanding and improving knowledge distillation
Jiaxi Tang, Rakesh Shivanna, Zhe Zhao, Dong Lin, Anima Singh, Ed H Chi, and Sagar Jain · 2020
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Taehyeon Kim, Jaehoon Oh, Nakyil Kim, Sangwook Cho, and Se-Young Yun · 2019
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Can gradient clipping mitigate label noise?
Aditya Krishna Menon, Ankit Singh Rawat, Sashank J Reddi, and Sanjiv Kumar · 2019
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey Hinton · 2019
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Human uncertainty makes classification more robust
Joshua C Peterson, Ruairidh M Battleday, Thomas L Griffiths, and Olga Russakovsky · 2019
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EfficientNet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Be your own teacher: Improve the performance of convolutional neural networks via self distillation
Linfeng Zhang, Jiebo Song, Anni Gao, Jingwei Chen, Chenglong Bao, and Kaisheng Ma · 2019
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Revisiting knowledge distillation via label smoothing regularization
Li Yuan, Francis EH Tay, Guilin Li, Tao Wang, and Jiashi Feng · 2020
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Self-distillation as instance-specific label smoothing
Zhilu Zhang and Mert R Sabuncu · 2020
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Distilling effective supervision from severe label noise
Zizhao Zhang, Han Zhang, Sercan O Arik, Honglak Lee, and Tomas Pfister · 2020
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Characterizing structural regularities of labeled data in overparameterized models
Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, and Michael C Mozer · 2021
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A statistical perspective on distillation
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S2sd: Simultaneous similarity-based self-distillation for deep metric learning
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