A State-of-the-Arts and Prospective in Neural Style Transfer

Abstract

Artistic style transfer is one the enormous research fields in computer vision. It is a technique to synthesize a content image and the style together to form an stylized image. Various popular applications like Prisma, Deepart, Pikazoapp provide a platform to synthesis images in order to produce very exciting artistic style. These applications use Convolutional Neural Networks to transform the images in an artistic style. Prisma photo editor has more than 250 modern art filters and 110 million users. Hence, the amount of images being synthesized is huge and computation requires to perform the task efficiently is large. Abundant CNN architectures like VGG16, VGG19, GoogleNet etc are introduced to solve this problem of heavy computations. The system helps users to transform their images into artworks using style of famous artists for stirring image filters, and it might be useful for artists to have an indication about the art. This paper presents the fundamental concepts, classification of Style Transfer and major progress towards Neural Style Transfer.