Authors
Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
Centre for Integrative Neuroscience, University of Tubingen; Bernstein Center for Computational Neuroscience; Graduate School of Neural Information Processing, University of Tubingen; Max Planck Institute for Biological Cybernetics; Baylor College of Medicine
Portals
Abstract
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.