Performance Analysis of Various Generative Adversarial Network using Dog image Dataset

Abstract

Generative Adversarial Network is a novel concept for a general purpose solution to Deep Fake Image generation. These networks learn mapping from input image to output image and also assign value in loss function for the same mapping. We demonstrate that this approach is effective to synthesize images from labelled images, and colorizing images, and other tasks. We have investigate performance of three different types of model i.e. simple GAN, DC-GAN, BIG-GAN, which have provided different results with generation of different loss function on the same dataset i.e. Stanford Dogs Dataset. In this paper, we have investigated the performance of models by using inception score and also track the loss function at different stages (epochs).