In the previous chapter, we learned about leveraging Generative Adversarial Networks (GANs) to generate realistic images. In this chapter, we will learn about leveraging GANs to manipulate images. We will learn about two variations of generating images using GANs – supervised and unsupervised methods. In the supervised method, we will provide the input and output pair combinations to generate images based on an input image, which we will learn about in the Pix2Pix GAN. In the unsupervised method, we will specify the input and output, however, we will not provide one-to-one correspondence between the input and output, but expect the GAN to learn the structure of the two classes, and convert an image from one class to another, which we will learn about in CycleGAN.
Another class of unsupervised image manipulation involves generating images...
