Designing and Implementing a Method of Data Augmentation Using Machine Learning

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.

Abstract licence: CC BY-NC

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