  We explore the method of style transfer presented in the article "A Neural
Algorithm of Artistic Style" by Leon A. Gatys, Alexander S. Ecker and Matthias
Bethge (arXiv:1508.06576).
  We first demonstrate the power of the suggested style space on a few
examples. We then vary different hyper-parameters and program properties that
were not discussed in the original paper, among which are the recognition
network used, starting point of the gradient descent and different ways to
partition style and content layers. We also give a brief comparison of some of
the existing algorithm implementations and deep learning frameworks used.
  To study the style space further we attempt to generate synthetic images by
maximizing a single entry in one of the Gram matrices $\mathcal{G}_l$ and some
interesting results are observed. Next, we try to mimic the sparsity and
intensity distribution of Gram matrices obtained from a real painting and
generate more complex textures.
  Finally, we propose two new style representations built on top of network's
features and discuss how one could be used to achieve local and potentially
content-aware style transfer.
