Generative adversarial networks (GANs) have been extensively studied in the
past few years. Arguably their most significant impact has been in the area of
computer vision where great advances have been made in challenges such as
plausible image generation, image-to-image translation, facial attribute
manipulation and similar domains. Despite the significant successes achieved to
date, applying GANs to real-world problems still poses significant challenges,
three of which we focus on here. These are: (1) the generation of high quality
images, (2) diversity of image generation, and (3) stable training. Focusing on
the degree to which popular GAN technologies have made progress against these
challenges, we provide a detailed review of the state of the art in GAN-related
research in the published scientific literature. We further structure this
review through a convenient taxonomy we have adopted based on variations in GAN
architectures and loss functions. While several reviews for GANs have been
presented to date, none have considered the status of this field based on their
progress towards addressing practical challenges relevant to computer vision.
Accordingly, we review and critically discuss the most popular
architecture-variant, and loss-variant GANs, for tackling these challenges. Our
objective is to provide an overview as well as a critical analysis of the
status of GAN research in terms of relevant progress towards important computer
vision application requirements. As we do this we also discuss the most
compelling applications in computer vision in which GANs have demonstrated
considerable success along with some suggestions for future research
directions. Code related to GAN-variants studied in this work is summarized on
this https URL

对 GAN 领域中的高质量图像生成、图像生成的多样性和稳定训练等三个实际问题的研究进展进行了详细回顾和分类，同时讨论了 GAN 在计算机视觉中最成功的应用并提出了未来的研究方向。