Image style transfer using convolutional neural networks based on transfer learning Online publication date: Fri, 22-Mar-2019
by Varun Gupta; Rajat Sadana; Swastikaa Moudgil
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 5, No. 1, 2019
Abstract: The purpose of an image style transfer system is to extract the semantic image content from the target image and then using a texture transfer procedure display the semantic content of target image in the style of the source image. The uphill task in this context is to render the semantic content of an image but with the advent of convolutional neural networks, image representations have been made much more explicit. In this work, we explore the method for image style transfer using transfer learning from pre-trained models of convolutional neural networks (CNN). Use of these models gives us the power to produce images of a high perceptual quality that are a union of the content of an arbitrary image and the appearance of renowned artworks. Further, this paper compares pre-trained CNN models for image style transfer task and highlights the potential of CNN to deliver appealing images using modern manipulation techniques.
Online publication date: Fri, 22-Mar-2019
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