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.
@article{arxiv.1603.03417,
title = {Texture Networks: Feed-forward Synthesis of Textures and Stylized Images},
author = {Dmitry Ulyanov and Vadim Lebedev and Andrea Vedaldi and Victor Lempitsky},
journal= {arXiv preprint arXiv:1603.03417},
year = {2016}
}