Abstract
Generative adversarial networks (GANs), introduced by Goodfellow et al. in 2014, have revolutionized the fields of machine learning and artificial intelligence. These networks have the unique ability to generate authentic synthetic data based on a given training dataset. The applications of GANs span across diverse domains, including the generation of new images, augmentation of existing datasets, imputation of missing data, and the creation of innovative models, such as completing 3D models from 2D data. Their utility extends to fields ranging from forensic science to biomedical research, where GANs have shown remarkable success in enhancing data-driven tasks. This chapter aims to provide an in-depth exploration of the role and impact of GANs across various sectors, emphasizing their practical applications and the underlying methodologies that drive their success.