A comprehensive survey on generative adversarial networks

Generative Adversarial Networks (GANs) are a class of neural network architectures that have been used to generate a wide variety of realistic data, including images, videos, and audio. GANs consist of two main components: a generator network, which produces new data, and a discriminator network, which attempts to distinguish the generated data from real data. The two networks are trained in a competitive manner, with the generator trying to produce data that can fool the discriminator, and the discriminator trying to correctly identify the generated data. Since their introduction in 2014, GANs have been applied to a wide range of tasks, such as image synthesis, image-to-image translation, and text-to-image synthesis. GANs have also been used in various fields such as computer vision, natural language processing, and speech recognition. Despite their success, GANs have several limitations and challenges, including mode collapse, where the generator produces only a limited number of distinct samples, and instability during training. Several methods have been proposed to address these challenges, including regularization techniques, architectural modifications, and alternative training algorithms. Overall, GANs have proven to be a powerful tool for generating realistic data, and research on GANs is an active area of study in the field of machine learning. This survey paper aims to provide an overview of the GANs architecture and its variants, applications and challenges, and the recent developments in GANs.