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Data augmentation (DA) is fundamental against overfitting in large convolutional neural networks, especially with a limited training dataset.
Data augmentation using learned transforms for one-shot medical image segmentation
A. Zhao, G. Balakrishnan, F. Durand, J. V. Guttag, and A. V. Dalca · 1909
Earlier work this paper cites.
A simple weight decay can improve generalization
A. Krogh and J. A. Hertz · 1992
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Curriculum learning
Y. Bengio, J. Louradour, R. Collobert, and J. Weston · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Discriminative clustering by regularized information maximization
A. Krause, P. Perona, and R. G. Gomes · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
Earlier work this paper cites.
Deep big multilayer perceptrons for digit recognition
D. C. Ciresan, U. Meier, L. M. Gambardella, and J. Schmidhuber · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Learning convolutional neural networks from few samples
R. Wagner, M. Thom, R. Schweiger, G. Palm, and A. Rothermel · 2013
Earlier work this paper cites.
Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. Goodfellow, J. Shlens, and C. Szegedy · 2015
Earlier work this paper cites.
Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Dreaming more data: Class-dependent distributions over diffeomorphisms for learned data augmentation
S. Hauberg, O. Freifeld, A. B. L. Larsen, J. Fisher, and L. Hansen · 2016
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Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Unsupervised and semi-supervised learning with categorical generative adversarial networks
J. T. Springenberg · 2016
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Triple generative adversarial nets
L. Chongxuan, T. Xu, J. Zhu, and B. Zhang · 2017
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Good semi-supervised learning that requires a bad gan
Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov · 2017
Revisiting unreasonable effectiveness of data in deep learning era
C. Sun, A. Shrivastava, S. Singh, and A. Gupta · 2017
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A bayesian data augmentation approach for learning deep models
T. Tran, T. Pham, G. Carneiro, L. Palmer, and I. Reid · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Augmenting image classifiers using data augmentation generative adversarial networks
A. Antoniou, A. Storkey, and H. Edwards · 2018
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Data augmentation instead of explicit regularization
A. Hernández-García and P. König · 2018
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Dataset augmentation in feature space
T. DeVries and G. Taylor · 2017
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Improved regularization of convolutional neural networks with cutout
T. DeVries and G. W. Taylor · 2017
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Smart augmentation learning an optimal data augmentation strategy
J. Lemley, S. Bazrafkan, and P. Corcoran · 2017
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Conditional image synthesis with auxiliary classifier gans
A. Odena, C. Olah, and J. Shlens · 2017
Cited alongside, same era.
The effectiveness of data augmentation in image classification using deep learning
L. Perez and J. Wang · 2017
Cited alongside, same era.
Learning to compose domain-specific transformations for data augmentation
A. J. Ratner, H. Ehrenberg, Z. Hussain, J. Dunnmon, and C. Ré · 2017
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A. Hernández-García and P. König · 2018
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
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Exploring the limits of weakly supervised pretraining
D. Mahajan, R. B. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
T. Miyato, S.-i. Maeda, S. Ishii, and M. Koyama · 2018
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Jointly optimize data augmentation and network training: Adversarial data augmentation in human pose estimation
X. Peng, Z. Tang, F. Yang, R. S. Feris, and D. Metaxas · 2018
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Rendergan: Generating realistic labeled data
L. Sixt, B. Wild, and T. Landgraf · 2018
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mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2018
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Dada: Deep adversarial data augmentation for extremely low data regime classification
X. Zhang, Z. Wang, D. Liu, and Q. Ling · 2019
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