Conditional generative adversarial networks in computer vision: an introduction and outlook

Abstract

Conditional Generative Adversarial Networks (CGAN) is an architecture-variant Generative Adversarial Network (GAN) that inputs conditional data into generator and discriminator simultaneously. CGAN has been applied in a wide range of fields in recent years. One of the most famous usages is its application in computer vision, which enables to perform images and videos transformation with specified conditions. In this paper, we first introduce the structure of GAN, which supports every CGAN system, and the basic CGAN that has been demonstrated in computer vision. Then, we review different implementations of CGAN in two major computer vision domains, i.e., the image processing domain and the videos processing domain. Besides, we review three representative research orientations in each domain, which all implement CGAN or variants of CGAN. Moreover, there is a comparison or declaration of several outstanding approaches in each direction.