新媒体艺术是什么？有哪些知名的作品？
解释最全面、最细致、最好的是最近的长篇译文（里面包含2篇Google研究博客的技术文章），有中英文对照，嵌有大量的链接和图片： 下面仅摘取译文总六部分之中的第三部分，译文版权归原译者 “姬汉旧邦” 所有：---------------------------------------- Google Research Blog
《Google研究网志》其一 Inceptionism: Going Deeper into Neural Networks
Inceptionism：更加深入地探究神经网络 Posted: Wednesday, June 17, 2015 发文日期：周三，2015-6-17 Posted by Alexander Mordvintsev, Software Engineer, Christopher Olah, 
Software Engineering Intern and Mike Tyka, Software Engineer 作者： Alexander Mordvintsev，软件工程师； Christopher Olah，软件工程师实习生； Mike Tyka，软件工程师 Update - 13/07/2015 更新日期：2015-7-13 Images in this blog post are licensed by Google Inc. under a Creative Commons Attribution 4.0 International License. However, images based on places by MIT Computer Science and AI Laboratory require additional permissions from MIT for use.
本博文中的图像为 Google 公司所有，采用知识共享署名4.0版本国际许可协议授权。不过，基于MIT计算机科学与人工智能实验室（做出的）的图像还需要另外向MIT申请使用许可。 Artificial Neural Networks have spurred remarkable recent progress in and .
 But even though these are very useful tools based on well-known 
mathematical methods, we actually understand surprisingly little of why 
certain models work and others don’t. So let’s take a look at some 
simple techniques for peeking inside these networks. 人工神经网络在 和 方面已经取得了显著的最新进展。但即使这些都是非常有用的基于知名数学方法的工具，我们实际上还是对为何某些模型能起作用而其他不能知之甚少。所以，让我们以简单的技术来窥探下这些网络里面（的东西）。 We train an artificial neural network by showing it millions of training examples and until it gives the classifications we want. The network typically 
consists of 10-30 stacked layers of artificial neurons. Each image is 
fed into the input layer, which then talks to the next layer, until 
eventually the “output” layer is reached. The network’s “answer” comes 
from this final output layer. 我们是这样训练一个人工神经网络的：向其展示数以百万计的训练样本并 ，直到它能给出我们想要的分类结果。该网络通常由 10~30 层堆叠的人工神经元组成。每幅图像都由输入层喂入（送入），然后递入下一层，直到最后抵达“输出”层。网络的“答案”就来自于这个最终的输出层。 One of the challenges of neural networks is understanding what exactly 
goes on at each layer. We know that after training, each layer 
progressively extracts higher and higher-level features of the image, 
until the final layer essentially makes a decision on what the image 
shows. For example, the first layer maybe looks for edges or corners. 
Intermediate layers interpret the basic features to look for overall 
shapes or components, like a door or a leaf. The final few layers 
assemble those into complete interpretations—these neurons activate in 
response to very complex things such as entire buildings or trees. 神经网络的一个挑战是要理解在每一层到底都发生了什么事。我们知道在（经过）训练之后，每一层会逐步提取越来越高级的图像特征，直到由最后一层做出显示为何的决定（本质上）。例如，第一层也许在寻找边缘或拐角。中间层分析基本的特征来寻找整体形状（即大体轮廓）或其组成部分，比如一扇门或一片叶子。最后几层将那些（要素）组合成完整的解释——（如此，）这些神经元对非常复杂的东西诸如整个建筑物或树木就有了反应。 One way to visualize what goes on is to turn the network upside down and
 ask it to enhance an input image in such a way as to elicit a 
particular interpretation. Say you want to know what sort of image would
 result in “Banana.” Start with an image full of random noise, then 
gradually tweak the image towards what the neural net considers a banana
 (see related work in , , , ).
 By itself, that doesn’t work very well, but it does if we impose a 
prior constraint that the image should have similar statistics to 
natural images, such as neighboring pixels needing to be correlated. 视觉化此过程的一种方法是将网络上下颠倒，并且要求它采用这种方法以引出一个特定的解释来增强输入的图像。假设（比如说）你想知道什么样的图像会导致“香蕉”这样的结果出现。从一幅充满随机噪点的图像着手，然后逐渐调整图像朝着神经网络认定其为香蕉的方向变化（见相关工作： , , , ）。本来效果并不怎么好，但如果我们施加先验约束就不一样了，即图像应该有类似的对自然图像统计的信息，比如邻近像素需要相互关联。 So here’s one surprise: neural networks that were trained to 
discriminate between different kinds of images have quite a bit of the 
information needed to generate images too. Check out some more examples 
across different classes: 因而这里有一个惊喜：被训练用以区分不同类型图像的神经网络也有相当多的信息需要去生成图像。查看一下（下面）更多不同类别的例子： Why is this important? Well, we train networks by simply showing them 
many examples of what we want them to learn, hoping they extract the 
essence of the matter at hand (e.g., a fork needs a handle and 2-4 
tines), and learn to ignore what doesn’t matter (a fork can be any 
shape, size, color or orientation). But how do you check that the 
network has correctly learned the right features? It can help to 
visualize the network’s representation of a fork. 为什么这个很重要？呃，我们通过简单向它们展示很多我们想让其学习的范例来训练网络，希望他们提取所关注事物的要素（例如，一个分叉需要一个柄和2~4个
尖齿），并学会忽略不相关的东西（分叉可以是任意形状、大小、颜色或方向）。但是如何检查网络（是不是）已经学到了对的特征呢？它可以帮助视觉化网络一个
分叉的表达。 Indeed, in some cases, this reveals that the neural net isn’t quite 
looking for the thing we thought it was. For example, here’s what one 
neural net we designed thought dumbbells looked like: 的确，在某些情况下，这揭示了神经网络并不是在寻找我们以为它在（寻找的）东西。例如，下面这个我们设计去思考哑铃的神经网络看起来像： There are dumbbells in there alright, but it seems no picture of a 
dumbbell is complete without a muscular weightlifter there to lift them.
 In this case, the network failed to completely distill the essence of a
 dumbbell. Maybe it’s never been shown a dumbbell without an arm holding
 it. Visualization can help us correct these kinds of training mishaps. 在那儿是有哑铃，但似乎没有一个图像中的哑铃是完整的，那儿并没有一个肌肉发达的举重运动员要来举起它们。在这种情况下，网络未能完全提取到哑铃的要素。也许它还从来没被展示过一个无手臂握着的哑铃（图片）。视觉化能帮助我们纠正这些训练事故。 Instead of exactly prescribing which feature we want the network to 
amplify, we can also let the network make that decision. In this case we
 simply feed the network an arbitrary image or photo and let the network
 analyze the picture. We then pick a layer and ask the network to 
enhance whatever it detected. Each layer of the network deals with 
features at a different level of abstraction, so the complexity of 
features we generate depends on which layer we choose to enhance. For 
example, lower layers tend to produce strokes or simple ornament-like 
patterns, because those layers are sensitive to basic features such as 
edges and their orientations. 替代确切地规定我们想要网络去放大的特征，我们也可以让网络（自己）做决定。在这种情况下，我们简单地给网络喂入任意的一幅图像或照片，并让网络分析。然
后我们挑选出一层，要求网络增强任何它能检测到的东西。网络的各层（神经元）会处理图片中不同抽象程度的特征，所以我们生成的特征其复杂性取决于我们选择
去增强哪一层。例如，较低的层会倾向于产生笔触或者简单的装饰性图案，因为那些层对基本的特征比如边缘和它们的方向很敏感。 Left: Original photo by Zachi Evenor. Right: processed by Günther Noack, Software Engineer
左图：原始图像，由 Zachi Evenor 摄制；右图：处理后的图像，由软件工程师 Günther Noack 制作 Left: Original painting by Georges Seurat. Right: processed images by Matthew McNaughton, Software Engineer
左图：Georges Seurat 的原始画作；右图：由软件工程师 Matthew McNaughton 处理后的图像 If we choose higher-level layers, which identify more sophisticated 
features in images, complex features or even whole objects tend to 
emerge. Again, we just start with an existing image and give it to our 
neural net. We ask the network: “Whatever you see there, I want more of 
it!” This creates a feedback loop: if a cloud looks a little bit like a 
bird, the network will make it look more like a bird. This in turn will 
make the network recognize the bird even more strongly on the next pass 
and so forth, until a highly detailed bird appears, seemingly out of 
nowhere. 如果我们选择更高级别、可以识别图像中更复杂特征的层，（那么）复杂的特征抑或整个对象往往就能显现出来。还有，我们只是以现有的图像开始，将其递入我们
的神经网络中。我们要求网络：“不管你看到的是什么，我都想要更多！”这样就创建了一个反馈循环：如果云朵看着有点像一只鸟，（那么）神经网络将会把它变
得更像鸟。这反过来将使网络在下一步以及更多步识认鸟时更加强烈，直到出现一个高度精细（具体）的鸟，（而它）似乎不知是从哪儿冒出来的。 The results are intriguing—even a relatively simple neural network can 
be used to over-interpret an image, just like as children we enjoyed 
watching clouds and interpreting the random shapes. This network was 
trained mostly on images of animals, so naturally it tends to interpret 
shapes as animals. But because the data is stored at such a high 
abstraction, the results are an interesting remix of these learned 
features. （研究）结果很是有趣——即使一个相对简单的神经网络也可以被用于过分解读图像，就像孩童时代我们喜欢看云朵、解读（出现的）随机形状一样。这个网络（之前）主要是以动物的图像被训练的（另译：由于之前主要是以动物的图像来训练这个神经网络的），因此自然它就倾向于把（一些）形状解释成动物。但由于数据被存储的如此高度抽象，结果就出现了这些习得特征的一个有趣的杂糅。 Of course, we can do more than cloud watching with this technique. We 
can apply it to any kind of image. The results vary quite a bit with the
 kind of image, because the features that are entered bias the network 
towards certain interpretations. For example, horizon lines tend to get 
filled with towers and pagodas. Rocks and trees turn into buildings. 
Birds and insects appear in images of leaves. 当然，我们可以做的远不止运用了这种技术的云朵。我们还可以把它应用（推广）到任何种类的图像。（而）一种图像的（处理）结果大异，是因为输入的（众多）特征造成了（神经）网络的偏差，使其朝着某些（方向）解析。例如，地平线往往会充斥着塔楼和宝塔（佛塔），岩石和树木变成了建筑物，鸟类和昆虫出现在叶子的图像中（即 AI 对叶子和虫鸟有点傻傻分不清）。 The original image influences what kind of objects form in the processed image.
原始图像会影响处理后的图像中的形成的那些对象。 This technique gives us a qualitative sense of the level of abstraction 
that a particular layer has achieved in its understanding of images. We 
call this technique “Inceptionism” in reference to the used. See our for more pairs of images and their processed results, plus some cool video animations. 这种技术给我们提供了一种对抽象层次的定性感受，即特定的某层已经取得了对图像（怎样）的理解。根据运用的 ，我们称这种技术为“Inceptionism”。想要更多对图像及其处理结果的图片，参见我们的 ，里面还有一些很酷的视频动画。 We must go deeper: Iterations
我们须得更深入些：迭代 If we apply the algorithm iteratively on its own outputs and apply some 
zooming after each iteration, we get an endless stream of new 
impressions, exploring the set of things the network knows about. We can
 even start this process from a random-noise image, so that the result 
becomes purely the result of the neural network, as seen in the 
following images: 如果我们把此算法反复应用在其自身的输出上，并在每次迭代后应用一些缩放，我们就能得到了无尽的新效果（数据）流，探索网络了解（知晓）的一众事物。我们甚至可以从一幅随机噪点（噪声）图像开始这个过程，以便（经处理后的）结果完全变成神经网络的结果，如下面的图像所示： Neural net “dreams”— generated purely from random noise, using a network trained on places by MIT Computer Science and AI Laboratory. See our Inceptionism gallery for hi-res versions of the images above and more (Images marked “Places205-GoogLeNet” were made using this network).
神经网络“梦想”——纯粹地由随机的噪声生成，使用的是经麻省理工学院计算机科学与人工智能实验室训练的网络。想要上面以及更多图像的高分辨率版本，可以移步到我们的 Inceptionism 相册（以“Places205-GoogLeNet”标记的图像是利用这个网络制作的）。 The techniques presented here help us understand and visualize how 
neural networks are able to carry out difficult classification tasks, 
improve network architecture, and check what the network has learned 
during training. It also makes us wonder whether neural networks could 
become a tool for artists—a new way to remix visual concepts—or perhaps 
even shed a little light on the roots of the creative process in 
general. 这里介绍的技术（可以）帮助我们理解以及视觉化神经网络是如何执行困难的分类任务的，改进网络体系结构，并检查在训练期间网络都学到了什么。它还让我们好
奇神经网络是否可以成为艺术家的工具——一种再混合视觉概念的新方法——或许即使通常（只有）一点点光透到创意过程的根基上。 Labels: , , 标签： 、 、 -------------------------------------------- Google Research Blog
《Google研究网志》其二 DeepDream - a code example for visualizing Neural Networks
Deep Dream，可视化神经网络的代码范例 Posted: Wednesday, July 01, 2015 发文时间：周三，2015-7-01 Posted by Alexander Mordvintsev, Software Engineer, Christopher Olah, 
Software Engineering Intern and Mike Tyka, Software Engineer 作者： Alexander Mordvintsev，软件工程师； Christopher Olah，软件工程师实习生； Mike Tyka，软件工程师 Two weeks ago we blogged about a visualization tool designed to help us 
understand how neural networks work and what each layer has learned. In 
addition to gaining some insight on how these networks carry out 
classification tasks, we found that this process also generated some 
beautiful art. 两周前我们发表了一篇博文，其中阐述了一个可视化（视觉）工具被设计用来帮助我们理解神经网络的工作原理以及每层学到了什么东西。除为了获悉这些（神经）网络是如何进行分类任务（作业）的，我们还发现这个过程也可以生成一些漂亮的艺术作品。 Top: Input image. Bottom: output image made using a network trained on places by . 上图：导入的图像；下图：使用 训练的神经网络做出的输出图像 We have seen a lot of interest and received some great questions, from 
programmers and artists alike, about the details of how these 
visualizations are made. We have decided to open source the code we used
 to generate these images in an , so now you can make neural network inspired images yourself! 我们看到了（大众对此的）极大兴趣，也收到了一些好的问询，来自程序员和艺术家们的（反馈）差不多，都问到了这些视觉化怎样实现的细节。我们已经决定开放在 中用于生成这些图像的源代码，因此现在你可以自己做些神经网络灵感的图像了！ The code is based on and uses available open source packages, and is designed to have as few
 dependencies as possible. To get started, you will need the following 
(full details in the notebook): 此代码基于 并使用了可获取的开源（软件）包，也设计尽可能地少用依赖。要开始使用它，你还需要下列的东西（手册里含有所有的细节）： · , , , , or a scientific python distribution such as or . · deep learning framework #-------------------我---是---分---割---线------------------- · 、 、 、 、像 或 这样的用于科学研究的python分发包； · 深度学习框架 Once you’re set up, you can supply an image and choose which layers in 
the network to enhance, how many iterations to apply and how far to zoom
 in. Alternatively, different pre-trained networks can be plugged in. 一旦配置好了，你就可以输入一幅图像，选择要增强网络中的哪些层、迭代多少次、把画面拉到多近（即放大）。而且，你还可以选择（经历）不同预先训练的网络。 It'll be interesting to see what imagery people are able to generate. If
 you post images to Google+, Facebook, or Twitter, be sure to tag them 
with #deepdream so other researchers can check them out too. 看看人们能做出何种图像会很有意思，如果你要在 Google+、非死不可或推特上发布图片，记得加上 #deepdream 标签。如此，其他人就也能切克闹一下。 Labels: , , 标签： 、 、