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AI学习语义的风格转换 – 译学馆
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AI学习语义的风格转换

AI Learns Semantic Style Transfer | Two Minute Papers #177

亲爱的学霸们 这是由Károly Zsolnai-Fehér带来的两分钟论文
Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér.
风格转换是机器学习和人工智能研究中一个让人惊叹的区域
Style transfer is an amazing area in machine learning and AI research,
它接收两幅图片作为输入
where we take two images.
图像一是作为输入的相片 图像二即为想得到的风格
Image number one is an input photograph, and image number two, is the desired style.
输出的过程是使图像一能够满足
And the output of this process is the content of image number one with the style of image
图像二的风格
number two.
这是第一篇打开了研究史上惊人的新领域的论文
This first paper opened up an incredible new area of research.
据结果显示 在过去两年中出现了大量不同的变体
As a result, a ton of different variants have emerged in the last two years.
前馈风格转换可得到接近实时的结果 时间连贯风格转换是针对视频的
Feedforward style transfer for close to real-time results, temporally coherent style transfer
以及很多 很多
for videos, and much, much more.
这不仅优于现有的技术 还开阔了
And this one not only outperforms previously existing techniques, but also broadens the
风格转换的应用的视野
horizon of possible style transfer applications.
显然 人们擅长这类工作是因为人们能够理解
And obviously, a human would be best at doing this because a human has an understanding
所看到的图像内容
of the objects seen in these images.
现在 握紧你手中的论文 因为这种方法的主要目标就是对于风格转换
And now, hold on to your papers, because the main objective of this is method to create
创造出有语义意义的结果
semantically meaningful results for style transfer.
它在那些外表看起来可能完全不同 但有一些
It is meant to do well with input image pairs that may look completely different visually,
相似语义成分的一组输入图像上 实现得很好
but have some semantic components that are similar.
例如 一张人脸图片和一张虚拟角色图片
For instance, a photograph of a human face and a drawing of a virtual character is excellent
就是一个非常恰当的例子
example of that.
在这种情况下 学习算法能识别出她们都有鼻子
In this case, this learning algorithm recognizes that they both have noses and uses this valuable
并在风格转换过程中使用这个有价值的信息
information in the style transfer process.
因此 它有三个超酷的应用程序
As a result, it has three super cool applications.
第一种 它可以转换我们了解和喜欢的常规图片的风格
First, the regular photo to style transfer that we all know and love.
第二种 它也可以交换两种输入图像的风格
Second, it is also capable of swapping the style of two input images.
第三种 请抓紧你手中的论文 因为这将对图片
Third, and hold on to your papers because this is going to be even more insane.
进行更加疯狂的设计或速写
Style or sketch to photo.
在这里 我们还要再加一点 所以第四种 可以支持照片之间的颜色转换
And we have a plus one here as well, so, fourth, it also supports color transfer between photographs,
创建出令人惊讶的展现时间推移的视频
which will allow creating amazing time-lapse videos.
我总是试图吸引身边的学霸们看这些论文 所以 为了论文更好的宣传
I always try to lure you Fellow Scholars into looking at these papers, so make sure to have
一定要看看这篇文章哟!
a look at the paper for some more results on this.
在这里 你可以看到 这种方法与其他几种技术的对比
And you can see here that this method was compared to several other techniques, for
例如 你可以看到周期一致性论文和PatchMatch算法
instance, you can see the cycle consistency paper and PatchMatch.
这真是个令人激动的时刻 因为我们的视频已经播出超过170集了
And this is one of those moments when I get super happy, because more than a 170 episodes
在这些连载视频中 我们不仅欣赏到这些高质量的效果
into the series, we can not only appreciate the quality of these new results, but
还回顾了先前视频中的两种算法
we also had previous episodes about both of these algorithms.
相关链接还会一如既往放在视频说明中 一定要看看啊
As always, the links are available in the video description, make sure to have a look,
这里有很多有趣的东西!
it’s going to be a lot of fun.
项目的源代码也可以看到
The source code of this project is also available.
我们还有很多讲述电脑图形系列的剧集
We also have a ton of episodes on computer graphics in the series, make sure to have
也一定不要错过哟!
a look at those as well.
我常常收到AI视频忠实粉丝发来的电子邮件
Every now and then I get e-mails from viewers who say that they came for the AI videos,
以防万一 我还观看了近期有关电脑绘图的一集 真的很有吸引力
and just in case, watched a recent episode on computer graphics and were completely hooked.
感谢您的收看和大力支持 我们下期再见!
Thanks for watching and for your generous support, and I’ll see you next time!

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视频概述

这是第一篇打开了图片风格转换新领域的论文,文中为您讲述了最新的几种风格转换的样式,很有意思的哟~

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