Designing and Implementing a Method of Data Augmentation Using Machine Learning

Aren K. Mayilyan

doi:10.51408/1963-0073

Authors

Aren K. Mayilyan National Polytechnic University of Armenia

DOI:

https://doi.org/10.51408/1963-0073

Keywords:

Machine Learning, Convolutional Neural Networks, Data Augmentation, burn degrees

Abstract

Efficiency of neural network (NN) models depend on the parameters given and the input data. Due to the complexity of environmental conditions and limitations the data for NN models, especially for the case of images, can be insufficient. To overcome this problem data augmentation has been used to enlarge the dataset. The task is to generate diverse set of images from a small set of images for NN training. Due to data augmentation transformation, 3105 new images out of 345 input data were created for classification, detection and image segmentation.

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(04 Oct 2021) [Online]. Available: https://en.wikipedia.org/wiki/Box_blur

(04 Oct 2021) [Online]. Available: https://en.wikipedia.org/wiki/Gaussian_blur