  Recent fast style transfer methods use a pre-trained convolutional neural
network as a feature encoder and a perceptual loss network. Although the
pre-trained network is used to generate responses of receptive fields effective
for representing style and content of image, it is not optimized for image
style transfer but rather for image classification. Furthermore, it also
requires a time-consuming and correlation-considering feature alignment process
for image style transfer because of its inter-channel correlation. In this
paper, we propose an end-to-end learning method which optimizes an
encoder/decoder network for the purpose of style transfer as well as relieves
the feature alignment complexity from considering inter-channel correlation. We
used uncorrelation loss, i.e., the total correlation coefficient between the
responses of different encoder channels, with style and content losses for
training style transfer network. This makes the encoder network to be trained
to generate inter-channel uncorrelated features and to be optimized for the
task of image style transfer which maintained the quality of image style only
with a light-weighted and correlation-unaware feature alignment process.
Moreover, our method drastically reduced redundant channels of the encoded
feature and this resulted in the efficient size of structure of network and
faster forward processing speed. Our method can also be applied to cascade
network scheme for multiple scaled style transferring and allows user-control
of style strength by using a content-style trade-off parameter.
