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  Texture Synthesis Using Convolutional Neural Networks

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.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-7AB6-A Version Permalink: http://hdl.handle.net/21.11116/0000-0000-7AB7-9
Genre: Conference Paper

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 Creators:
Gatys, LA, Author
Ecker, AS1, 2, 3, Author              
Bethge, M1, 2, Author              
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
3Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              

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 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.

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 Dates: 2016
 Publication Status: Published in print
 Pages: -
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 Rev. Method: -
 Identifiers: BibTex Citekey: GatysEB2015
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Title: Twenty-Ninth Annual Conference on Neural Information Processing Systems (NIPS 2015)
Place of Event: Montréal, Canada
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Title: Advances in Neural Information Processing Systems 28
Source Genre: Proceedings
 Creator(s):
Cortes, C., Editor
Lawrence, N.D., Editor
Lee, D.D., Editor
Sugiyama, M., Editor
Garnett, R., Editor
Garnett, R., Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 262 - 270 Identifier: -