representation. For the same image input, the Laplacian transfer uses 7 levels, thus going for coarser style too. In each such pyramidal level, the VGG stacks 2 to 3 convolutions, while the Laplacian uses only one.

Another aspect is related to the used filters. For the Laplacian filters, the weights are preset, always having the same values, while for the neural style transfer, the weights are learned while trying to classify on the ImageNet task. It has been shown that the CNN lower level filters are similar to standard gradient filters [@fischer2014], yet those from upper levels have no such correspondence. In this point, one should note that CNN architectures use multiple filters in parallel (the VGG from 64 in the bottom layer to 512 in the upper ones), while the Laplacian only 1. This huge difference ensures that a much richer description is available for the neural transfer.

The last aspect taken into account for the comparison is the procedure used for matching the distributions. The original Neural Style transfer goes for minimizing the MMD, which is to ensure that the multi-dimensional feature distributions of the image to change and of the reference image do match from a statistical 