Software Implementation of the Generative Adversarial Network Algorithm.

Abstract

The article discusses the software implementation of the Generative Adversarial Network (GAN) algorithm for image synthesis tasks. It presents the stages of data preprocessing, the design of the generator and discriminator architectures, and the training methodology using combined loss function, the quality of the generator's output is evaluateds. During the training of the model, significant results were achieved, confirmed by the quantitative quality metrics PSNR and SSIM. The prospects for further development include optimizing the learning process to increase the stability of the model, introducing modifications to the GAN architecture, as well as expanding the functionality of the system by supporting more complex and diverse types of input data.