부산대학교 도서관

The Generative Adversarial Networks (GANs) approach has seen major advancements in computer vision and other practical domains. To generate realistic data, GAN is most powerful deep learning architecture out of diverse generating models. The advancement and expansion of GANs over the decade are observed in detail in this comprehensive paper, with a particular focus on technological innovations, computational developments, datasets, and applications. In addition, it studies latent areas for future research to fully achieve the potential of GANs and explores the major obstacles that prevent their prevalent use. This paper provides a comprehensive understanding of the GANs impact on computer vision through the use of detailed comparative examines, illustrations, and data-driven perceptions. [ABSTRACT FROM AUTHOR]