Here we demonstrate that the feature space of random shallow convolutional
neural networks (CNNs) can serve as a surprisingly good model of natural
textures. Patches from the same texture are consistently classified as being
more similar then patches from different textures. Samples synthesized from the
model capture spatial correlations on scales much larger then the receptive
field size, and sometimes even rival or surpass the perceptual quality of state
of the art texture models (but show less variability). The current state of the
art in parametric texture synthesis relies on the multi-layer feature space of
deep CNNs that were trained on natural images. Our finding suggests that such
optimized multi-layer feature spaces are not imperative for texture modeling.
Instead, much simpler shallow and convolutional networks can serve as the basis
for novel texture synthesis algorithms.

研究表明，随机浅层卷积神经网络的特征空间可以作为自然纹理的模型，可以用于纹理合成算法，并且相较于深层 CNNs 模型，浅层 CNNs 模型具有相当的性能。

使用随机卷积滤波器的浅层卷积神经网络纹理合成

Texture Synthesis Using Shallow Convolutional Networks with Random  Filters

Here we introduce a new model of natural textures based on the feature spaces
of convolutional neural networks optimised for object recognition. Samples from
the model are of high perceptual quality demonstrating the generative power of
neural networks trained in a purely discriminative fashion. Within the model,
textures are represented by the correlations between feature maps in several
layers of the network. We show that across layers the texture representations
increasingly capture the statistical properties of natural images while making
object information more and more explicit. The model provides a new tool to
generate stimuli for neuroscience and might offer insights into the deep
representations learned by convolutional neural networks.

介绍了一种基于卷积神经网络特征空间的自然纹理新模型，可生成具有高感知质量的样本，可作为神经科学刺激或深度卷积神经网络学习的新工具。