论文标题
一项有关生成对抗网络的调查：变体，应用和培训
A Survey on Generative Adversarial Networks: Variants, Applications, and Training
论文作者
论文摘要
由于其出色的数据生成能力，生成模型通过一个名为“生成对抗网络（GAN）”的新实用框架在无监督学习的领域中引起了极大的关注。许多GAN的模型都提出了，并且在计算机视觉和机器学习的各个领域都出现了一些实际应用。尽管Gan取得了良好的成功，但仍然存在稳定训练的障碍。问题是由于NASH平衡，内部协变量转移，模式崩溃，消失的梯度以及缺乏适当的评估指标。因此，稳定的培训是GAN成功的不同应用中的关键问题。在此，我们调查了不同研究人员提出的几种培训解决方案，以稳定GAN培训。我们调查（i）原始的GAN模型及其修改的经典版本，（ii）对不同域中各种GAN应用的详细分析，（iii）有关各种GAN训练障碍以及培训解决方案的详细研究。最后，我们讨论了几个新问题，并概述了该主题。
The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability. Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning. Despite GAN's excellent success, there are still obstacles to stable training. The problems are due to 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 GAN. Herein, we survey several training solutions proposed by different researchers to stabilize GAN training. We survey, (I) the original GAN model and its modified classical versions, (II) detail analysis of various GAN applications in different domains, (III) detail study about the various GAN training obstacles as well as training solutions. Finally, we discuss several new issues as well as research outlines to the topic.
