ARTISTIC STYLE TRANSFER USING CONVOLUTIONAL NEURAL NETWORKS

Abstract

One of the exciting research field has implemented known as Neural Style Transfer, which is a technique to transform images in an artistic way. Two images are taken as input image namely style image and content image to transform another base image with the help of optimization technique. This NST can be done with the help of Convolutional Neural Networks model as many researchers have tried to achieve good results using CNN network architecture. One of the famous and efficient pre-trained architecture is VGG16 and Gatys et al. [2] were able to generated good results based upon the VGG model. [2] Many famous Mobile and Web applications like DeepArt, Prisma and Pikazoapp have used these models to transformed images in an artistic way. [6] [27] We primarily have discussed different Neural Style Transfer techniques then we have classified the artistic style transfer. We have implementation the model in Keras with the pretrained CNN model that is VGG19 where we have adjusted the hyperparameters and transformation coefficients. VGG19 model has been trained on ImageNet dataset and we used it for feature extraction where for testing we have used two datasets namely Caltech101 and Caltech256. The fundamentals of NST is also discussed in depth literature survey which can be found in chapter 2.