International Journal of Science, Engineering and TechnologyAuthors: Anakha R Varma, Angelina Sennichan, Ujjwal Agrawal, Shrey Keyal, Shashank Modi, Dr. Umamaheswari M
Abstract: Generative Adversarial Networks (GANs) have rapidly become a cornerstone of modern artificial intelligence research since their introduction in 2014 [1][2][3]. By pitting two neural networks—the generator and the discriminator—against each other in a zero-sum game, GANs have enabled breakthroughs in realistic data generation, computer vision, natural language processing, and creative AI [2][10]. This adversarial learning paradigm not only allows machines to learn distributions of real-world data but also to generate synthetic data that is almost indistinguishable from reality [1][3]. In this paper, we provide a structured overview of GANs, covering their core architectures, evolution into advanced variants, real-world applications, algorithmic workflow, and challenges [10][14]. We also analyze open research issues and future directions to guide practitioners and researchers [5][15]. To make this study comprehensive, references are drawn from 15 peer-reviewed sources across computer science, engineering, and applied domains, and illustrative figures of GAN workflow, algorithm, and application scenarios are included to aid conceptual understanding [8][11].
DOI: https://doi.org/10.5281/zenodo.17452368
