Image Augmentation with Neural Style Transfer

This paper proposes and evaluates a deep learning model that will be used for image augmentation, which makes use of transfer learning in order to learn the characteristics of the content and style of the training images, create random style embeddings via learned multivariate normal distribution, and ultimately generate images to extend the original dataset.