May 2024
Intermediate to advanced
373 pages
6h 6m
Chinese
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第10章 风格迁移
绘画展现了一种内容与风格的复杂互动关系。而另一方面，照片是一种透视和光线的组合。当两者相结合时，产生的结果是壮观且充满惊喜的，这个过程被称为艺术风格迁移。图10.1所示为一个艺术风格迁移的例子，输入图像拍摄自位于德国蒂宾根的内卡河畔，而风格图像是凡·高的著名画作《星空》。产生的结果非常有趣，不是吗？
图10.1
仔细观察图10.1，可以发现右边的绘画风格图像似乎从左边的照片中提取了内容，绘画的风格、颜色和笔画模式生成了最终的结果。这个迷人的结果是Gatys等人发表的一篇迁移学习算法论文的成果，论文名为A Neural Algorithm for Artistic Style。我们将从实现算法的角度来讨论这篇论文的复杂之处，并学习如何亲自实现这项技术。
本章将重点利用深度学习和迁移学习来构建一个神经风格迁移系统。本章的重点内容如下所示：
- 理解神经风格迁移；
- 图像预处理方法论；
- 构建损失函数；
- 构建一个自定义优化器；
- 风格迁移实战。
本章内容包含与神经风格迁移、损失函数和优化方法相关的理论概念。除此之外，我们将使用一种实际操作方法来实现我们自己的神经风格迁移模型。本章中的代码可以在异步社区网站获取。
10.1 理解神经风格迁移
神经风格迁移是将参考图像的风格应用于特定的目标图像，而目标图像的原始内容保持不变的过程。此处风格指的是参考图像中出现的颜色、模式和纹理，而内容则被定义为目标图像的总体结构和高级组件。
在风格迁移中，主要目的是保留原始目标图像的内容，同时在目标图像上叠加或采用参考图像的风格。为了从数学的角度定义该概念，可以考虑3张图像：原始内容图像（表示为 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.