Image Generation
Image Generation
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DOI：
10.1007/978-1-4842-6150-7_13
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发表时间：
2020-11
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影响因子：
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通讯作者：
P. Sarang
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文献类型：
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作者：
P. Sarang
你有没有想过神经网络可以用来生成复杂的彩色图像？动漫怎么样？名人的脸怎么样？卧室怎么样？听起来是不是很有趣？所有这些都可以通过神经网络中最有趣的想法来实现，那就是生成对抗网络（GAN）。这个想法是由Ian J. Goodfellow在2014年提出和发展的。GAN创建的图像看起来如此真实的，以至于几乎不可能区分假的和真实的图像。需要注意的是，要生成这种性质的复杂图像，你需要大量的资源来训练网络，但它确实可以像你在学习本章时看到的那样工作。让我们来看看什么是GAN。
Did you ever imagine that neural networks could be used for generating complex color images? How about Anime? How about the faces of celebrities? How about a bedroom? Doesn’t it sound interesting? All these are possible with the most interesting idea in neural networks and that is Generative Adversarial Networks (GANs). The idea was introduced and developed by Ian J. Goodfellow in 2014. The images created by GAN look so real that it becomes practically impossible to differentiate between a fake and a real image. Be warned, to generate complex images of this nature, you would require lots of resources to train the network, but it does certainly work as you would see when you study this chapter. So let us look at what is GAN.