Evaluating GAN Image Generation Quality and Diversity

mage generation has become increasingly prevalent in recent times, with image-to-image translation emerging as a rapidly growing field. Generative Adversarial Networks (GANs) are commonly used in this context, where the aim is to generate images that closely resemble given input images. In this approach, the Generator and Discriminator, revolutionizing generative models in image synthesis. a Generator creates new images based on a probability distribution, while a Discriminator attempts to distinguish between real and generated images. This paper provides a comprehensive examination of four major GAN architectures introduced since the inception of GANs in 2014. The objective is to learn a mapping function G: X -¿ Y, such that the distribution Y is indistinguishable from the input distribution G(X). The paper includes quantitative comparisons against baseline models, demonstrating the superiority of our approach.