Advancements in Image Synthesis with Progressive Generative Adversarial Networks

An overview of the use of Generative Adversarial Networks (GANs) in computer vision—image synthesis and manipulation—is given in this study. A generator and a discriminator are two complex neural networks that are trained in competition to form GANs. Owing to the formidable capabilities of deep neural networks and their adversarial training approach, GANs exhibit the ability to generate realistic and plausible images, thereby demonstrating remarkable prowess across various applications in the realm of image synthesis and manipulation. This survey paper delves into recent GAN-related research. Working on the progressive GANs model technique, the authors find out how capable it is compared to other GANs algorithms. Building an image synthesis Progressive Generative Adversarial Networks (ProGANs) is a complex process with several steps that work together to produce high-quality pictures.