References

Cui, X., V. Goel and B. Kingsbury, 2015. Data augmentation for deep neural network acoustic modeling. IEEE. ACM. Trans. Audio Speech Lang. Process. TASLP., 23: 1469-1477.
CrossRef  |  Direct Link  |  

Dellana, R. and K. Roy, 2016. Data augmentation in CNN-based periocular authentication. Proceedings of the International Conference on Information Communication and Management (ICICM), October 29-31, 2016, IEEE, Hatfield, UK., ISBN:978-1-5090-3495-6, pp: 141-145.

Ding, J., B. Chen, H. Liu and M. Huang, 2016. Convolutional neural network with data augmentation for SAR target recognition. IEEE. Geosci. Remote Sens. Lett., 13: 364-368.
CrossRef  |  Direct Link  |  

Fawzi, A., H. Samulowitz, D. Turaga and P. Frossard, 2016. Adaptive data augmentation for image classification. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), September 25-28, 2016, IEEE, Phoenix, Arizona, ISBN:978-1-4673-9961-6, pp: 3688-3692.

Krizhevsky, A., I. Sutskever and G.E. Hinton, 2012. Image net classification with deep convolutional neural networks. Proc. Neural Inf. Process. Syst., 1: 1097-1105.

Lemley, J., S. Bazrafkan and P. Corcoran, 2017. Smart augmentation-learning an optimal data augmentation strategy. IEEE. Access, 5: 5858-5869.
CrossRef  |  Direct Link  |  

McLaughlin, N., D.J.M. Rincon and P. Miller, 2015. Data-augmentation for reducing dataset bias in person re-identification. Proceedings of the 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), August 25-28, 2015, IEEE, Karlsruhe, Germany, ISBN:978-1-4673-7632-7, pp: 1-6.

Oliveira, D.P.I., J.L.P. Medeiros and D.V.F. Sousa, 2016. A data augmentation methodology to improve age estimation using convolutional neural networks. Proceedings of the 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), October 4-7, 2016, IEEE, Sao Paulo, Brazil, ISBN:978-1-5090-3568-7, pp: 88-95.

Salamon, J. and J.P. Bello, 2017. Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE. Signal Process. Lett., 24: 279-283.
CrossRef  |  Direct Link  |  

Shen, X. and R. Messina, 2016. A method of synthesizing handwritten Chinese images for data augmentation. Proceedings of the 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), October 23-26, 2016, IEEE, Shenzhen, China, ISBN:978-1-5090-0981-7, pp: 114-119.

Wong, S.C., A. Gatt, V. Stamatescu and M.D. McDonnell, 2016. Understanding data augmentation for classification: When to warp?. Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), November 30-December2, 2016, IEEE, Gold Coast, Queensland, Australia, ISBN:978-1-5090-2896-2, pp: 1-6.

Zhang, C., P. Zhou, C. Li and L. Liu, 2015. A convolutional neural network for leaves recognition using data augmentation. Proceedings of the 2015 IEEE International Conference on Computer and Information Technology: Ubiquitous Computing and Communications: Dependable, Autonomic and Secure Computing: Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), October 26-28, 2015, IEEE, Liverpool, UK., ISBN:978-1-5090-0154-5, pp: 2143-2150.