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15 March 2019 Effective real-time augmentation of training dataset for the neural networks learning
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Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 110411I (2019) https://doi.org/10.1117/12.2522969
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany
Abstract
In this paper we study the real-time augmentation - method of increasing variability of training dataset during the learning process. We consider the most common label-preserving deformations, which can be useful in many practical tasks. Due to limitations of existing augmentation tools like increase in learning time or dependence on a specific platform, we developed own real-time augmentation system. Experiments on MNIST and SVHN datasets demonstrated the effectiveness of suggested approach - the quality of the trained models improves, and learning time remains the same as if augmentation was not used.
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Alexander V. Gayer, Yulia S. Chernyshova, and Alexander V. Sheshkus "Effective real-time augmentation of training dataset for the neural networks learning", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110411I (15 March 2019); https://doi.org/10.1117/12.2522969
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