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.

中文翻译：

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基于对抗学习的图像到图像转换：一项调查

摘要 最近，生成对抗网络（GAN）在各种计算机视觉任务中引起了广泛关注。GAN 通过对抗性学习为图像到图像的转换提供了一个新概念。近年来，已经提出了许多基于对抗性学习的方法，并取得了令人瞩目的成果。相关评论主要集中在基本 GAN 模型及其通用变体上；相比之下，本次调查旨在通过关注图像到图像转换场景来概述基于对抗性学习的方法。首先，简要回顾了基本的 GAN；接下来，相关方法大致分为对抗性风格迁移和对抗性图像恢复，例如超分辨率、图像修复和去雨。详细介绍和讨论了生成模型和损失函数的网络架构。最后，我们通过对趋势和挑战的分析来结束调查。