March 15, 2019

Effective real-time augmentation of training dataset for the neural networks learning

Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 110411I (2019)

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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