  In this work we investigate different avenues of improving the Neural
Algorithm of Artistic Style (by Leon A. Gatys, Alexander S. Ecker and Matthias
Bethge, arXiv:1508.06576).
  While showing great results when transferring homogeneous and repetitive
patterns, the original style representation often fails to capture more complex
properties, like having separate styles of foreground and background. This
leads to visual artifacts and undesirable textures appearing in unexpected
regions when performing style transfer.
  We tackle this issue with a variety of approaches, mostly by modifying the
style representation in order for it to capture more information and impose a
tighter constraint on the style transfer result.
  In our experiments, we subjectively evaluate our best method as producing
from barely noticeable to significant improvements in the quality of style
transfer.
