Neural Style学习1——简介

该项目是Github上面的一个开源项目，其利用卷积神经网络的理论，参照论文A Neural Algorithm of Artistic Style，可以实现一种效果：两张图片，一张取其内容，另一张取其风格，然后将两张融合到一起。

This is a torch implementation of the paper A Neural Algorithm of Artistic Style

by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge.

The paper presents an algorithm for combining the content of one image with the style of another image using

convolutional neural networks. Here's an example that maps the artistic style of

The Starry Night

onto a night-time photograph of the Stanford campus:

Applying the style of different images to the same content image gives interesting results.

Here we reproduce Figure 2 from the paper, which renders a photograph of the Tubingen in Germany in a

variety of styles:

Here are the results of applying the style of various pieces of artwork to this photograph of the

golden gate bridge:

Content / Style Tradeoff

The algorithm allows the user to trade-off the relative weight of the style and content reconstruction terms,

as shown in this example where we port the style of Picasso's 1907 self-portrait onto Brad Pitt:

Style Scale

By resizing the style image before extracting style features, we can control the types of artistic

features that are transfered from the style image; you can control this behavior with the -style_scale flag.

Below we see three examples of rendering the Golden Gate Bridge in the style of The Starry Night.

From left to right, -style_scale is 2.0, 1.0, and 0.5.

Multiple Style Images

You can use more than one style image to blend multiple artistic styles.

Clockwise from upper left: "The Starry Night" + "The Scream", "The Scream" + "Composition VII",

"Seated Nude" + "Composition VII", and "Seated Nude" + "The Starry Night"

Style Interpolation

When using multiple style images, you can control the degree to which they are blended:

Transfer style but not color

If you add the flag -original_colors 1 then the output image will retain the colors of the original image;

this is similar to the recent blog post by deepart.io.