  In this paper we propose a new method to get the specified network parameters
through one time feed-forward propagation of the meta networks and explore the
application to neural style transfer. Recent works on style transfer typically
need to train image transformation networks for every new style, and the style
is encoded in the network parameters by enormous iterations of stochastic
gradient descent. To tackle these issues, we build a meta network which takes
in the style image and produces a corresponding image transformations network
directly. Compared with optimization-based methods for every style, our meta
networks can handle an arbitrary new style within $19ms$ seconds on one modern
GPU card. The fast image transformation network generated by our meta network
is only 449KB, which is capable of real-time executing on a mobile device. We
also investigate the manifold of the style transfer networks by operating the
hidden features from meta networks. Experiments have well validated the
effectiveness of our method. Code and trained models has been released
https://github.com/FalongShen/styletransfer.

Computer Vision and Pattern Recognition