The Generative Models have gained considerable attention in unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to their outstanding data generation capability. Many GAN models have been proposed, and several practical applications have emerged in various domains of computer vision and machine learning. Despite GANs excellent success, there are still obstacles to stable training. The problems are Nash equilibrium, internal covariate shift, mode collapse, vanishing gradient, and lack of proper evaluation metrics. Therefore, stable training is a crucial issue in different applications for the success of GANs. Herein, we survey several training solutions proposed by different researchers to stabilize GAN training. We discuss (I) the original GAN model and its modified versions, (II) a detailed analysis of various GAN applications in different domains, and (III) a detailed study about the various GAN training obstacles as well as training solutions. Finally, we reveal several issues as well as research outlines to the topic.

中文翻译：

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生成对抗网络调查：变体、应用和训练

生成模型通过称为生成对抗网络 (GAN) 的新实用框架在无监督学习中获得了相当大的关注，因为它们具有出色的数据生成能力。已经提出了许多 GAN 模型，并在计算机视觉和机器学习的各个领域出现了一些实际应用。尽管 GAN 取得了巨大成功，但稳定训练仍然存在障碍。问题是纳什均衡、内部协变量偏移、模式崩溃、梯度消失以及缺乏适当的评估指标。因此，稳定训练是 GAN 在不同应用中取得成功的关键问题。在这里，我们调查了不同研究人员提出的几种训练解决方案，以稳定 GAN 训练。我们讨论（I）原始 GAN 模型及其修改版本，(II) 对不同领域的各种 GAN 应用的详细分析，以及 (III) 对各种 GAN 训练障碍以及训练解决方案的详细研究。最后，我们揭示了几个问题以及该主题的研究大纲。