Generative Adversarial Networks in Image Generation and Recognition

Generative Adversarial Network (GAN) is a class of Generative Machine Learning frameworks, which has shown remarkable promise in the field of synthetic data generation. GANs consist of a generative model and a discriminative model working in a game like contest to generate data with high levels of accuracy. This paper delves into the applications of GANs in the field of Image Generation and Recognition. We look into the advantages and challenges of using GANs, and the ongoing areas of research and improvements, and potential breakthroughs.