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Conference Paper

Texture Synthesis Using Convolutional Neural Networks

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Ecker,  AS
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bethge,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Gatys, L., Ecker, A., & Bethge, M. (2016). Texture Synthesis Using Convolutional Neural Networks. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, R. Garnett, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 28 (pp. 262-270). Red Hook, NY, USA: Curran.


Cite as: https://hdl.handle.net/21.11116/0000-0000-7AB6-A
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.