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Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis

Abstract:

The recent work of Gatys et al., who characterized the style of an image by the statistics of convolutional neural network filters, ignited a renewed interest in the texture generation and image stylization problems. While their image generation technique uses a slow optimization process, recently several authors have proposed to learn generator neural networks that can produce similar outputs in one quick forward pass. While generator networks are promising, they are still inferior in visual...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/CVPR.2017.437

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
New College
Role:
Author
Publisher:
Institute of Electrical and Electronics Engineers
Host title:
30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017).
Journal:
30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). More from this journal
Publication date:
2017-11-01
Acceptance date:
2017-03-03
DOI:
ISSN:
1063-6919
Pubs id:
pubs:820121
UUID:
uuid:ac8980a6-8bb7-400c-8291-b3d83c336289
Local pid:
pubs:820121
Deposit date:
2018-01-24

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