生成对抗网络改进角度与应用研究综述

生成对抗网络(GAN)作为一种新兴的生成式模型，逐渐发展应用于图像生成、三维重构、跨模态转换等领域，有效解决了常规卷积神经网络在图像生成类任务方面效率低下的问题，填补了深度学习在图像生成领域上的短板。为了帮助后续研究人员快速并全面了解GAN,根据近年来的文献对GAN的改进模型进行梳理。首先从网络结构、目标函数两个角度介绍了GAN的基本原理，然后对GAN的各种衍生模型从改进角度、应用类型两个方面进行详细的阐述和总结，分别从主观定性、客观定量和任务专项评估等角度对生成图像的质量和多样性进行归纳分析，最后讨论了GAN系列模型近年来的一些核心问题与最新研究进展，并分析了未来的发展趋势。

Review of research on improvement and application of generative adversarial networks

Generative adversarial network(GAN), as an emerging generative model, has been gradually developed and applied in the fields of image generation, 3D reconstruction, cross-modal conversion, etc. It effectively solves the problem of inefficiency of conventional convolutional neural networks in image-generating tasks and fills the shortage of deep learning in the field of image generation. In order to help subsequent researchers quickly and comprehensively understand GAN, this paper sorted out the improved model of GAN based on the literature in recent years. It firstly introduced the basic principles of GAN from two perspectives of network structure and objective function, then elaborated and summarized various derivative models of GAN from two major perspectives of improvement and application types. Secondly it summarized and analyzed the quality and diversity of generated images from the perspectives of subjective qualitative, objective quantitative and task-specific evaluation. Finally, this paper discussed some core issues and latest research progress of GAN series models in recent years and analyzed the future development trend.