Formation of alternative approaches to noise generation in gan networks

Abstract

This work discusses the problem of developing alternative approaches to generating noise in generative adversarial networks (GAN). Various noise generation techniques are important for training GAN networks as they help improve the quality of the generated data and the stability of training. This article provides an overview of current noise generation methods and discusses an approach based on the Pandas library in the Python programming language for generating, storing, and mixing noises.