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Data augmentation has led to substantial improvements in the performance and generalization of deep models, and remain a highly adaptable method to evolving model architectures and varying amounts of data---in particular, extremely scarce amounts of available training data.
Randaugment: Practical data augmentation with no separate search
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Möbius Transformations in Several Dimensions
L. Ahlfors · 1989
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Complex valued neural network with möbius activation function
N. Özdemir, B. B. İskender, and N. Y. Özgür · 2011
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Deep cnn ensemble with data augmentation for object detection
J. Guo and S. Gould · 2015
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Improved regularization of convolutional neural networks with cutout
T. DeVries and G. W. Taylor · 2017
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The effectiveness of data augmentation in image classification using deep learning
L. Perez and J. Wang · 2017
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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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Tiny imagenet challenge
J. Wu, Q. Zhang, and G. Xu · 2017
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Understanding convolution for semantic segmentation
P. Wang, P. Chen, Y. Yuan, D. Liu, Z. Huang, X. Hou, and G. Cottrell · 2018
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Mixmatch: A holistic approach to semi-supervised learning
D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel · 2019
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Population based augmentation: Efficient learning of augmentation policy schedules
D. Ho, E. Liang, I. Stoica, P. Abbeel, and X. Chen · 2019
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Enabling explainable fusion in deep learning with fuzzy integral neural networks
M. A. Islam, D. T. Anderson, A. Pinar, T. C. Havens, G. Scott, and J. M. Keller · 2019
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Improved mixed-example data augmentation
C. Summers and M. J. Dinneen · 2019
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H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2017
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Random erasing data augmentation
Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang · 2017
Cited alongside, same era.
Hyperbolic neural networks
O. Ganea, G. Bécigneul, and T. Hofmann · 2018
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Data augmentation instead of explicit regularization
A. Hernández-García and P. König · 2018
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Data augmentation by pairing samples for images classification
H. Inoue · 2018
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Between-class learning for image classification
Y. Tokozume, Y. Ushiku, and T. Harada · 2018
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Autoaugment: Learning augmentation strategies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le
Cited in the paper.
Data augmentation using random image cropping and patching for deep cnns
R. Takahashi, T. Matsubara, and K. Uehara · 2019
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Adatransform: Adaptive data transformation
Z. Tang, X. Peng, T. Li, Y. Zhu, and D. N. Metaxas · 2019
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Unsupervised data augmentation
Q. Xie, Z. Dai, E. Hovy, M.-T. Luong, and Q. V. Le · 2019
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Deep compositional spatial models
A. Zammit-Mangion, T. L. J. Ng, Q. Vu, and M. Filippone · 2019
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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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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
K. Sohn, D. Berthelot, C.-L. Li, Z. Zhang, N. Carlini, E. D. Cubuk, A. Kurakin, H. Zhang, and C. Raffel · 2020
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