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Data augmentation by mixing samples, such as Mixup, has widely been used typically for classification tasks.
Transformation invariance in pattern recognition—tangent distance and tangent propagation
P. Y. Simard, Y. A. LeCun, J. S. Denker, and B. Victorri · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Foundations of Machine Learning
M. Mohri, A. Rostamizadeh, F. Bach, and A. Talwalkar · 2012
Earlier work this paper cites.
Apac: Augmented pattern classification with neural networks
I. Sato, H. Nishimura, and K. Yokoi · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition, 2015
K. Simonyan and A. Zisserman · 2015
Earlier work this paper cites.
Importance weighted autoencoders
Y. Burda, R. Grosse, and R. Salakhutdinov · 2016
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
Cited alongside, same era.
Dropout rademacher complexity of deep neural networks
W. Gao and Z.-H. Zhou · 2016
Cited alongside, same era.
Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Improving deep learning using generic data augmentation
L. Taylor and G. Nitschke · 2017
Cited alongside, same era.
Between-class learning for image classification
Y. Tokozume, Y. Ushiku, and T. Harada
Cited in the paper.
Learning from between-class examples for deep sound recognition
Y. Tokozume, Y. Ushiku, and T. Harada
Cited in the paper.
Data augmentation by pairing samples for images classification
H. Inoue · 2018
Later among the works it cites.
Manifold mixup: Learning better representations by interpolating hidden states
V. Verma, A. Lamb, C. Beckham, A. Najafi, A. Courville, I. Mitliagkas, and Y. Bengio · 2018
Later among the works it cites.
mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2018
Later among the works it cites.
Mixup as locally linear out-of-manifold regularization
H. Guo, Y. Mao, and R. Zhang · 2019
Closest in time.
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