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We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise.
Long short-term memory
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On the design of loss functions for classification: theory, robustness to outliers, and savageboost
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Learning SVMs from sloppily labeled data
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Learning object categories from internet image searches
R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman · 2010
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Random classification noise defeats all convex potential boosters
P. M. Long and R. A. Servedio · 2010
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Composite binary losses
M. D. Reid and R. C. Williamson · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
J. Duchi, E. Hazan, and Y. Singer · 2011
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Learning word vectors for sentiment analysis
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Harvesting image databases from the web
F. Schroff, A. Criminisi, and A. Zisserman · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Learning to label aerial images from noisy data
V. Mnih and G. E. Hinton · 2012
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Learning with noisy labels
N. Natarajan, I. S. Dhillon, P. K. Ravikumar, and A. Tewari · 2013
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Classification with asymmetric label noise : Consistency and maximal denoising
C. Scott, G. Blanchard, and G. Handy · 2013
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Learning everything about anything: Webly-supervised visual concept learning
S. Divvala, A. Farhadi, and C. Guestrin · 2014
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Classification in the Presence of Label Noise: A Survey
B. Frénay and M. Verleysen · 2014
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Training deep neural networks on noisy labels with bootstrapping
S. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan, and A. Rabinovich · 2014
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Class proportion estimation with application to multiclass anomaly rejection
Training convolutional networks with noisy labels
S. Sukhbaatar, J. Bruna, M. Paluri, L. Bourdev, and R. Fergus · 2015
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Machine Learning via Transitions
B. van Rooyen · 2015
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Learning with symmetric label noise: The importance of being unhinged
B. van Rooyen, A. K. Menon, and R. C. Williamson · 2015
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Learning from massive noisy labeled data for image classification
T. Xiao, T. Xia, T. Yang, C. Huang, and X. Wang · 2015
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Deep residual learning for image recognition
K. He, X. Zhang., S. Ren, and J. Sun · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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T. Sanderson and C. C. Scott · 2014
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Semi-supervised sequence learning
A. M. Dai and Q. V. Le · 2015
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Efficient and robust automated machine learning
M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum, and F. Hutter · 2015
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Making risk minimization tolerant to label noise
A. Ghosh, N. Manwani, and P. S. Sastry · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Deep networks with stochastic depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Weinberger · 2016
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Deep learning without poor local minima
K. Kawaguchi · 2016
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The unreasonable effectiveness of noisy data for fine-grained recognition
J. Krause, B. Sapp, A. Howard, H. Zhou, A. Toshev, T. Duerig, J. Philbin, and L. Fei-Fei · 2016
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Classification with noisy labels by importance reweighting
T. Liu and D. Tao · 2016
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Learning from binary labels with instance-dependent corruption
A. Menon, B. van Rooyen, and N. Natarajan · 2016
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Loss factorization, weakly supervised learning and label noise robustness
G. Patrini, F. Nielsen, R. Nock, and M. Carioni · 2016
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Mixture proportion estimation via kernel embedding of distributions
H. G. Ramaswamy, C. Scott, and A. Tewari · 2016
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