Generative Adversarial Networks (GANs): Introduction and vista

Abstract

A particular type of deep learning model [11] used for unsupervised learning is called a generative adversarial network (GAN), which was initially propound by Ian Goodfellow and others in a 2014 [22]. GANs have been successful in generating an extensive data, including images, audio, and text, and have many potential applications, such as generating synthetic training data, improving data augmentation, and creating new content in fields such as art and music. Despite their success, GANs have also faced challenges, including the difficulty of training them and the potential ethical implications of generating synthetic data. Nevertheless, GANs have remained an active area of research and have continued to evolve and improve over time. The goal of this research is to offer a thorough review of GANs. The theoretical foundations of GANs and a number of GAN variants are covered in this paper. After that, we explore the standards for evaluating GANs.