  Neural style transfer has drawn broad attention in recent years. However,
most existing methods aim to explicitly model the transformation between
different styles, and the learned model is thus not generalizable to new
styles. We here attempt to separate the representations for styles and
contents, and propose a generalized style transfer network consisting of style
encoder, content encoder, mixer and decoder. The style encoder and content
encoder are used to extract the style and content factors from the style
reference images and content reference images, respectively. The mixer employs
a bilinear model to integrate the above two factors and finally feeds it into a
decoder to generate images with target style and content. To separate the style
features and content features, we leverage the conditional dependence of styles
and contents given an image. During training, the encoder network learns to
extract styles and contents from two sets of reference images in limited size,
one with shared style and the other with shared content. This learning
framework allows simultaneous style transfer among multiple styles and can be
deemed as a special `multi-task' learning scenario. The encoders are expected
to capture the underlying features for different styles and contents which is
generalizable to new styles and contents. For validation, we applied the
proposed algorithm to the Chinese Typeface transfer problem. Extensive
experiment results on character generation have demonstrated the effectiveness
and robustness of our method.
