Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably the revolutionary techniques are in the area of computer vision such as plausible image generation, image to image translation, facial attribute manipulation and similar domains. Despite the significant success achieved in computer vision field, applying GANs over real-world problems still have three main challenges: (1) High quality image generation; (2) Diverse image generation; and (3) Stable training. Considering numerous GAN-related research in the literature, we provide a study on the architecture-variants and loss-variants, which are proposed to handle these three challenges from two perspectives. We propose loss and architecture-variants for classifying most popular GANs, and discuss the potential improvements with focusing on these two aspects. While several reviews for GANs have been presented, there is no work focusing on the review of GAN-variants based on handling challenges mentioned above. In this paper, we review and critically discuss 7 architecture-variant GANs and 9 loss-variant GANs for remedying those three challenges. The objective of this review is to provide an insight on the footprint that current GANs research focuses on the performance improvement. Code related to GAN-variants studied in this work is summarized on https://github.com/sheqi/GAN_Review.
翻译：过去几年来,人们广泛研究了对抗性网络(GANs),认为革命技术属于计算机视觉领域,例如貌似图像生成、图像转换、面部属性操控和类似领域。尽管在计算机视觉领域取得了巨大成功,将GANs应用于现实世界问题,但在应用GANs解决现实世界问题方面仍面临三大挑战:(1) 高质量图像生成;(2) 多样化图像生成;(3) 稳定培训。考虑到文献中与GAN有关的大量研究,我们从两个角度对拟议应对这三个挑战的建筑-变量和损失-变量进行了研究。我们提出对最受欢迎的GANs分类的损失和结构-变量,并讨论了以这两个方面为重点的潜在改进。虽然对GANs的一些审查没有侧重于基于应对上述挑战对GAN-变量的审查。在本文中,我们审查并批判地讨论了7个架构-变量GANs和9个损失-变量GANs,以弥补这三个挑战。本次审查的目的是提供对当前GAN/GANs相关研究的足迹的洞察GANs。