Image Generation with GANs

Abstract

This chapter explores the functionalities of Generative Adversarial Networks (GANs), which are employed for the purpose of image synthesis, surpassing conventional methods in their advancements. GANs use a generator and discriminator network for increasing image quality through adverse training. This introduction discusses the progression of methods used for image generation before the advent of GANs. Additionally, it provides an overview of the fundamental principles behind GAN training. While more sophisticated GAN architectures, such as DCGANs, utilize CNN to ensure stability, StyleGAN improves the accuracy of synthesis. Conditional GANs and impact of having domain specific using SRGAN, Text-to-Image to help boost the image quality undergone at major phases. Datasets and pre-trained models such as ImageNet, CelebA, CIFAR correspondingly in significant for training data. The chapter also addresses the application in art, fashion and garment design, medicine for imaging to medical its usage educations media affects creativity and AI training.