主要观点总结
本文介绍了生成对抗网络(GAN)的原理和应用。GAN由生成网络和判别网络组成,通过两者之间的竞争学习数据分布。除了用于计算机视觉中的图像/视频分布建模外,该框架还可通过示例定义视觉概念。它大大减少了各种计算机视觉问题对手动设计目标函数的依赖。本教程将概述生成对抗网络的研究概况,包括最新的理论研究和训练技巧,以及GAN在视觉领域的应用。
关键观点总结
关键观点1: GAN的原理和组成
生成对抗网络由生成网络和判别网络组成,通过两者之间的竞争学习数据分布。
关键观点2: GAN的应用领域
生成对抗网络可广泛应用于计算机视觉中的图像/视频分布建模,并通过示例定义视觉概念,减少对手动设计目标函数的依赖。
关键观点3: GAN的研究概况
本教程将介绍生成对抗网络的研究概况,包括最新的理论研究、训练技巧以及GAN在视觉领域的应用。
正文
Generative adversarial network (GAN) has recently emerged as a promising generative modeling approach. It consists of a generative network and a discriminative network. Through the competition between the two networks, it learns to model the data distribution. In addition to modeling the image/video distribution in computer vision problems, the framework finds use in defining visual concept using examples. To a large extent, it eliminates the need of hand-crafting objective functions for various computer vision problems. In this tutorial, we will present an overview of generative adversarial network research. We will cover several recent theoretical studies as well as training techniques and will also cover several vision applications of generative adversarial networks.
链接:
https://github.com/mingyuliutw/cvpr2017_gan_tutorial
原文链接:
https://m.weibo.cn/1402400261/4134119480631730