深度学习是利用卷积网络的深层结构提取的信息，卷积网络目前主要用于图像识别分类技术，其实在其中间层中包含了丰富的有用信息，而这些正是风格迁移的基础。 如果研究 CNN 的各层级结构，会发现里面的每一层神经元的激活态都对应了一种特定的信息，越是底层的就越接近画面的纹理信息，如同物品的材质； 越是上层的，就越接近实际内容，如同物品的种类。 风格迁移就是把一张图片的底层信息和另一张图片的上层信息结合在一起的方法。用适当的数学方法，我们可以在卷积网络的中间层里提取图象内容有关的信息（content）和图象风格有关的信息（style）。
☆10Aug 25, 2021Updated 4 years ago
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