Adversarial-learning-based image-to-image transformation: A survey

Abstract

Recently, the generative adversarial network (GAN) has attracted wide attention for various computer vision tasks. GAN provides a novel concept for image-to-image transformation by means of adversarial learning. In recent years, numerous adversarial-learning-based methods have been proposed, and impressive results have been achieved. Related reviews have mainly focused on the basic GAN model and its general variants; in contrast, this survey aims to provide an overview of adversarial-learning-based methods by focusing on the image-to-image transformation scenario. First, a brief review of basic GAN is presented; next, the related approaches are roughly divided into adversarial style transfer and adversarial image restoration, e.g., super-resolution, image inpainting, and de-raining. The network architectures of generative models and loss functions are introduced and discussed in detail. Finally, we conclude the survey with an analysis of the trends and challenges.