A review on Generative Adversarial Networks for image generation - Trevisan de Souza, Vinicius Luis; Marques, Bruno Augusto Dorta; Batagelo, Harlen Costa; Gois, João Paulo (Computers & graphics, 2023)
Generative Adversarial Networks (GANs) are a type of deep learning architecture that uses two networks namely a generator and a discriminator that, by competing against each other, pursue to create realistic but previously unseen samples. They have become a popular research topic in recent years, particularly for image processing and synthesis, leading to many advances and applications in various fields. With the profusion of published works and interest from professionals of different areas, surveys on GANs are necessary, mainly for those who aim starting on this topic. In this work, we cover the basics and notable architectures of GANs, focusing on their applications in image generation. We also discuss how the challenges to be addressed in GANs architectures have been faced, such as mode coverage, stability, convergence, and evaluating image quality using metrics. [Display omitted] •A review on GANs for image generation, aiming at readers who are new to the area.•A comprehensive overview of GAN fundamentals, and methods to address the most common issues.•A detailed explanation of how various works applied GANs in image-based applications.•A discussion of future directions for this area.