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 as follows: (1) the generation of high quality images, (2) diversity of image generation, and (3) stabilizing 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 toward 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 toward critical 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. Codes related to the GAN-variants studied in this work is summarized on https://github.com/sheqi/GAN_Review.

中文翻译：

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计算机视觉中的生成对抗网络

生成对抗网络（GAN）在过去几年中得到了广泛的研究。可以说，它们最显着的影响是在计算机视觉领域，在诸如合理图像生成、图像到图像转换、面部属性操作和类似领域等挑战方面取得了巨大进步。尽管迄今为止取得了重大成功，但将 GAN 应用于现实世界的问题仍然存在重大挑战，我们在这里重点关注其中三个。它们如下：（1）高质量图像的生成，（2）图像生成的多样性，以及（3）稳定训练。着眼于流行的 GAN 技术在应对这些挑战方面取得的进展程度，我们在已发表的科学文献中对 GAN 相关研究的最新技术进行了详细回顾。我们根据 GAN 架构和损失函数的变化采用了一种方便的分类法，进一步构建了这篇评论。虽然迄今为止已经提出了一些关于 GAN 的评论，但没有人根据他们在解决与计算机视觉相关的实际挑战方面取得的进展来考虑该领域的现状。因此，我们回顾并批判性地讨论了最流行的架构变体和损失变体 GAN，以应对这些挑战。我们的目标是根据关键计算机视觉应用需求的相关进展，对 GAN 研究的现状进行概述和批判性分析。在此过程中，我们还讨论了计算机视觉中最引人注目的应用，在这些应用中 GAN 取得了相当大的成功，并对未来的研究方向提出了一些建议。在 https://github.com/sheqi/GAN_Review 上总结了与本工作中研究的 GAN 变体相关的代码。