Neural Style Transfer Using VGG19 and Alexnet

Art is the perfect way for people to express their emotions in a way that words are unable to do. By simply looking at art, we can understand a person’s creativity and thoughts. In former times, artists spent a great deal of time creating an image of varied styles. In the current deep learning era, we are able to create images of different styles as we prefer within a short period of time. Neural style transfer is the most popular and widely used deep learning application that applies the desired style to the content image, which in turn generates an output image that is a combination of both style and the content of the original image. In this paper we have implemented the neural style transfer model with two architectures namely Vgg19 and Alexnet. This paper compares the output-styled image and the total loss obtained through VGG19 and Alexnet architectures. In addition, three different activation functions are used to compare quality and total loss of output styled images within Alexnet architectures.