  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.
