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Texture networks: Feed-forward synthesis of textures and stylized images

Abstract:

Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods require a slow and memoryconsuming optimization process. We propose here an alternative approach that moves the computational burden to a learning stage. Given a single example of a texture, our approach trains compact feed-forward convolutional networks to generate multiple samples of the same texture of arbitrary size and to transfer...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
New College
Role:
Author
Publisher:
Association for Computing Machinery Publisher's website
Journal:
33rd International Conference on Machine Learning (ICML 2016) Journal website
Host title:
33rd International Conference on Machine Learning (ICML 2016)
Publication date:
2016-06-19
Acceptance date:
2016-04-24
Source identifiers:
666864
ISBN:
9781510829008
Pubs id:
pubs:666864
UUID:
uuid:76d0f6d6-00f4-4c9b-a6a8-b12c2e68b1a7
Local pid:
pubs:666864
Deposit date:
2018-11-26

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