  Neural style transfer (NST) is a powerful image generation technique that
uses a convolutional neural network (CNN) to merge the content of one image
with the style of another. Contemporary methods of NST use first or second
order statistics of the CNN's features to achieve transfers with relatively
little computational cost. However, these methods cannot fully extract the
style from the CNN's features. We present a new algorithm for style transfer
that fully extracts the style from the features by redefining the style loss as
the Wasserstein distance between the distribution of features. Thus, we set a
new standard in style transfer quality. In addition, we state two important
interpretations of NST. The first is a re-emphasis from Li et al., which states
that style is simply the distribution of features. The second states that NST
is a type of generative adversarial network (GAN) problem.
