October 2020
Intermediate to advanced
693 pages
16h 26m
Chinese
Content preview from 机器学习实战：基于Scikit-Learn、Keras 和TensorFlow （原书第2 版）
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510
｜
第
17
章
插值的结果。请注意，例如第 4 行和第 5 列中的鞋子是位于其上方和下方的两双鞋子之
间的很好插值。
图 17-14：语义插值
几年来，变分自动编码器非常流行，但是 GAN 最终占了领先位置，是因为它们能够生
成更加逼真的图像。因此，让我们将注意力转向 GAN。
17.9 生成式对抗网络
Ian Goodfellow 等人在 2014 年的论文
注 10
中提出了生成式对抗网络，尽管这个想法几乎
立刻使研究人员们兴奋不已，但还是花了几年时间才克服了训练 GAN 的一些困难。就
像许多伟大的想法一样，事后看起来似乎很简单：让神经网络相互竞争，希望这种竞争
能够促使它们变得更好。如图 17-15 所示，GAN 由两个神经网络组成：
1
生成器
以随机分布作为输入（通常是高斯分布），并输出一些数据（通常是图像）。你可以
将随机输入视为要生成的图像的潜在表征（即编码）。因此你可以看到，生成器提
供的功能与变分自动编码器中的解码器相同，并且可以使用相同的方式来生成新图
像（只需馈入一些高斯噪声，就会输出一个新图片）。但是我们很快就会看到，它
的训练方式大不相同。
注 10 ：Ian Goodfellow et al., “ Generative Adversarial Nets ” ,
Proceedings of the 27th International
Conference on Neural Information Processing Systems
2 (2014): 2672–2680. ...
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