前言

这是一篇关于GAN在计算机视觉领域的综述。

正文

生成对抗网络是一种基于博弈论的生成模型，其中神经网络用于模拟数据分布。应用领域：语言生成、图像生成、图像到图像翻译、图像生成文本描述、视频生成。GAN模型能够复制数据分布并生成合成数据，应用一定的标准偏差来创建新的、以前从未见过的数据。

图1显示了GAN架构是如何组成的。由于这种架构的复杂性，GANs在训练[16–18]过程中存在不稳定。这些模型中训练的不稳定性导致了模态崩溃等问题，因此人们对[19–23]的这类问题进行了研究。正如[24]所定义的，当GANs模型生成具有不同输入的相同类输出时，就会发生模式崩溃。

GAN调查通常集中在GAN模型结构[16,27]或它们在某些任务[28,29]中的应用上。本文主要聚焦在模型结构本身 。文章[34]这样的调查的重点是分析最先进的通用神经网络，并进一步分析各种网络的性能。此外，他们还提出了一套关于哪种损失函数最适合每种使用情况的建议。文章[35]关注的是过去几年不同的GAN的架构如何用于不同的问题，而文章[28]则展示了计算机视觉及其应用的不同架构。

文章调研总览

GAN网络的模型结构时间轴

GAN网络的损失函数时间轴

GAN网络的时间轴

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未完待续...