Understanding GANs

GANs (Generative Adversarial Networks) are a class of models where images are translated from one distribution to another. GANs are helpful in various use-cases, for example: enhancing image quality, photograph editing, image-to-image translation, clothing translation, etc. Nowadays, many retailers, fashion industries, media, etc. are making use of GANs to improve their business and relying on algorithms to do the task. There are many forms of GAN available serving different purposes, but in this article, we will focus on CycleGAN. Here we will see its working and implementation in PyTorch. CycleGAN learns the mapping of an image from source X to a target domain Y. Assume you have an aerial image of a city and want to convert in google maps image or the landscape image into a segmented image, but you don't have the paired images available, then there is GAN for you.