  Recent feed-forward neural methods of arbitrary image style transfer mainly
utilized encoded feature map upto its second-order statistics, i.e., linearly
transformed the encoded feature map of a content image to have the same mean
and variance (or covariance) of a target style feature map. In this work, we
extend the second-order statistical feature matching into a general
distribution matching based on the understanding that style of an image is
represented by the distribution of responses from receptive fields. For this
generalization, first, we propose a new feature transform layer that exactly
matches the feature map distribution of content image into that of target style
image. Second, we analyze the recent style losses consistent with our new
feature transform layer to train a decoder network which generates a style
transferred image from the transformed feature map. Based on our experimental
results, it is proven that the stylized images obtained with our method are
more similar with the target style images in all existing style measures
without losing content clearness.
