To what extent is the success of deep visualization due to the training?
Could we do deep visualization using untrained, random weight networks? To
address this issue, we explore new and powerful generative models for three
popular deep visualization tasks using untrained, random weight convolutional
neural networks. First we invert representations in feature spaces and
reconstruct images from white noise inputs. The reconstruction quality is
statistically higher than that of the same method applied on well trained
networks with the same architecture. Next we synthesize textures using scaled
correlations of representations in multiple layers and our results are almost
indistinguishable with the original natural texture and the synthesized
textures based on the trained network. Third, by recasting the content of an
image in the style of various artworks, we create artistic images with high
perceptual quality, highly competitive to the prior work of Gatys et al. on
pretrained networks. To our knowledge this is the first demonstration of image
representations using untrained deep neural networks. Our work provides a new
and fascinating tool to study the representation of deep network architecture
and sheds light on new understandings on deep visualization.

使用未训练的卷积神经网络，创建新的生成模型，用于图像重建、纹理合成和样式转换等深度可视化任务，并发现了新的深度网络结构表示学习的一些特性和规律。

利用随机权重的强大生成模型用于深度图像表示

A Powerful Generative Model Using Random Weights for the Deep Image  Representation

We can better understand deep neural networks by identifying which features
each of their neurons have learned to detect. To do so, researchers have
created Deep Visualization techniques including activation maximization, which
synthetically generates inputs (e.g. images) that maximally activate each
neuron. A limitation of current techniques is that they assume each neuron
detects only one type of feature, but we know that neurons can be multifaceted,
in that they fire in response to many different types of features: for example,
a grocery store class neuron must activate either for rows of produce or for a
storefront. Previous activation maximization techniques constructed images
without regard for the multiple different facets of a neuron, creating
inappropriate mixes of colors, parts of objects, scales, orientations, etc.
Here, we introduce an algorithm that explicitly uncovers the multiple facets of
each neuron by producing a synthetic visualization of each of the types of
images that activate a neuron. We also introduce regularization methods that
produce state-of-the-art results in terms of the interpretability of images
obtained by activation maximization. By separately synthesizing each type of
image a neuron fires in response to, the visualizations have more appropriate
colors and coherent global structure. Multifaceted feature visualization thus
provides a clearer and more comprehensive description of the role of each
neuron.

通过深度可视化技术，可以更好地了解深度神经网络。本文介绍了一种算法，通过合成每种类型的图像来清晰地揭示神经元的多重特征，并引入正则化方法使可视化结果更具解释性和清晰性。