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Datasets with significant proportions of noisy (incorrect) class labels present challenges for training accurate Deep Neural Networks (DNNs).
A simple general approach to inference about the tail of a distribution
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Maximum likelihood estimation of intrinsic dimension
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Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
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Visualizing data using t-SNE
Maaten, Laurens van der and Hinton, Geoffrey · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, Alex and Hinton, Geoffrey · 2009
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Deep sparse rectifier neural networks
Glorot, Xavier, Bordes, Antoine, and Bengio, Yoshua · 2011
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Reading digits in natural images with unsupervised feature learning
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Hinton, Geoffrey, Deng, Li, Yu, Dong, Dahl, George E., Mohamed, Abdel-rahman, Jaitly, Navdeep, Senior, Andrew, Vanhoucke, Vincent, Nguyen, Patrick, Sainath, Tara N., and Kingsbury, Brian · 2012
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Houle, Michael E., Kashima, Hisashi, and Nett, Michael · 2012
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Houle, Michael E · 2013
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Learning with noisy labels
Natarajan, Nagarajan, Dhillon, Inderjit S., Ravikumar, Pradeep K., and Tewari, Ambuj · 2013
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In search of the real inductive bias: On the role of implicit regularization in deep learning
Neyshabur, Behnam, Tomioka, Ryota, and Srebro, Nathan · 2014
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Training deep neural networks on noisy labels with bootstrapping
Reed, Scott, Lee, Honglak, Anguelov, Dragomir, Szegedy, Christian, Erhan, Dumitru, and Rabinovich, Andrew · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, Nitish, Hinton, Geoffrey E., Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan · 2014
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A closer look at memorization in deep networks
Arpit, Devansh, Jastrzebski, Stanisaw, Ballas, Nicolas, Krueger, David, Bengio, Emmanuel, Kanwal, Maxinder S., Maharaj, Tegan, Fischer, Asja, Courville, Aaron, Bengio, Yoshua, and Lacoste-Julien, Simon · 2017
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Robust loss functions under label noise for deep neural networks
Ghosh, Aritra, Kumar, Himanshu, and Sastry, PS · 2017
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Training deep neural-networks using a noise adaptation layer
Goldberger, Jacob and Ben-Reuven, Ehud · 2017
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Mentornet: Regularizing very deep neural networks on corrupted labels
Jiang, Lu, Zhou, Zhengyuan, Leung, Thomas, Li, Li-Jia, and Fei-Fei, Li · 2017
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Deep nets don’t learn via memorization
Krueger, David, Ballas, Nicolas, Jastrzebski, Stanislaw, Arpit, Devansh, Kanwal, Maxinder S, Maharaj, Tegan, Bengio, Emmanuel, Fischer, Asja, and Courville, Aaron · 2017
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Sukhbaatar, Sainbayar and Fergus, Rob · 2014
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Training convolutional networks with noisy labels
Sukhbaatar, Sainbayar, Bruna, Joan, Paluri, Manohar, Bourdev, Lubomir, and Fergus, Rob · 2014
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Estimating local intrinsic dimensionality
Amsaleg, Laurent, Chelly, Oussama, Furon, Teddy, Girard, Stéphane, Houle, Michael E., Kawarabayashi, Ken-ichi, and Nett, Michael · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
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Deep learning
LeCun, Yann, Bengio, Yoshua, and Hinton, Geoffrey · 2015
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Learning from massive noisy labeled data for image classification
Xiao, Tong, Xia, Tian, Yang, Yi, Huang, Chang, and Wang, Xiaogang · 2015
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Deep residual learning for image recognition
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2016
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Learning from noisy labels with distillation
Li, Yuncheng, Yang, Jianchao, Song, Yale, Cao, Liangliang, Luo, Jiebo, and Li, Jia · 2017
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Making neural networks robust to label noise: a loss correction approach
Patrini, Giorgio, Rozza, Alessandro, Menon, Aditya, Nock, Richard, and Qu, Lizhen · 2017
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Opening the black box of deep neural networks via information
Shwartz-Ziv, Ravid and Tishby, Naftali · 2017
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Toward robustness against label noise in training deep discriminative neural networks
Vahdat, Arash · 2017
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Learning from noisy large-scale datasets with minimal supervision
Veit, Andreas, Alldrin, Neil, Chechik, Gal, Krasin, Ivan, Gupta, Abhinav, and Belongie, Serge · 2017
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Understanding deep learning requires rethinking generalization
Zhang, Chiyuan, Bengio, Samy, Hardt, Moritz, Recht, Benjamin, and Vinyals, Oriol · 2017
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Characterizing adversarial subspaces using local intrinsic dimensionality
Ma, X., Li, B., Wang, Y., Erfani, S., Wijewickrema, S. Schoenebeck, G., Houle, M. E. Song, D., and Bailey, J · 2018
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On the information bottleneck theory of deep learning
Saxe, Andrew M., Bansal, Yamini, Dapello, Joel, Advani, Madhu, Kolchinsky, Artemy, Tracey, Brendan D., and Cox, David D · 2018
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Iterative learning with open-set noisy labels
Wang, Yisen, Liu, Weiyang, Ma, Xingjun, Bailey, James, Zha, Hongyuan, Song, Le, and Xia, Shu-Tao · 2018
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