Face Generation Using General Adversial Networks

Abstract

Face generation is a difficult task with numerous uses in security (for example, creating portraits from descriptions), styling, and entertainment. In this study, we investigate generating faces conditioned on identification using modifications to generative adversarial networks (GANs). In the realm of artificial intelligence, deep learning has had significant success, and several deep learning models have been created. One of the deep learning models, Generative Adversarial Networks (GAN), which was proposed based on zero-sum game theory and has become a new research hotspot. The importance of the GAN model variation is to provide more accurate and realistic data by using unsupervised learning to obtain the data distribution. Due to their vast potential for use in areas including language processing, video processing, and image and vision computing, GANs are currently the subject of extensive research. This project introduces the history of the GAN, theoretical models, and extensional variants of GANs, where the variants can either modify the fundamental structures of the original GAN or further optimise it. The typical uses of GANs are then described. Finally, a summary of the current GAN issues and a description of the models' upcoming work are provided.