GANs in computer vision - Introduction to generative learning

What is the difference with autoencoders? What is the fundamental training algorithm of a GAN model? How can we make it learn meaningful representations? In what computer vision application can it be useful? How can one design one for her/his problem? We will address all these questions and much much! In this review article series, we will focus on a plethora of GANs for computer vision applications. Specifically, we will slowly build upon the ideas and the principles that led to the evolution of generative adversarial networks (GAN). We will encounter different tasks such as conditional image generation, 3D object generation, video synthesis. Let's start with reviewing our contents of the first part!