  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.
