Abstract
The paper explores an important area of artificial intelligence — Generative Adversarial Networks (GANs), which are used to create high-quality artificial data samples. GANs have undergone significant development and application in various sectors, including the processing of graphical data. The report focuses on the practical use of GANs and their architecture. It discusses the fundamental principles of GAN operation, highlights the advantages and disadvantages, including issues with training, vanishing gradients, and convergence oscillations, and describes measures to overcome these problems. It also examines current research in the field of GANs and their applications in various domains, including cybersecurity, medicine, forensics, and computer vision. Practical results from the report's authors regarding their own GAN experiments, optimization, and architecture improvements are presented. The research aims to analyze the architectural features of GANs to enhance their training process
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