Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods requires a slow and memory-consuming optimization process. We propose here an alternative approach that moves the computational burden to a...

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Bibliographic Details
Published in:arXiv.org 2016-03
Main Authors: Ulyanov, Dmitry, Lebedev, Vadim, Vedaldi, Andrea, Lempitsky, Victor
Format: Article
Language:English
Subjects:
Artificial neural networks
Optimization
Texture
Weight reduction
Online Access:Get full text
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Summary:Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods requires a slow and memory-consuming 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 artistic style from a given image to any other image. The resulting networks are remarkably light-weight and can generate textures of quality comparable to Gatys~et~al., but hundreds of times faster. More generally, our approach highlights the power and flexibility of generative feed-forward models trained with complex and expressive loss functions.
ISSN:2331-8422