Exploring the Neural Algorithm of Artistic Style

In this work we explore the method of style transfer presented in [1]. 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 [1], 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, an idea similar to [2] is used to generate synthetic images by maximizing a single entry in one of the Gram matrices Gl 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.