reduced number of iterations are performed while over–imposing the style of an artistic painting and the content from a photograph onto a new image, according to the neural style transfer introduced by Gatys et al. [-@Gatys:2015].

Overall, this paper claims several contributions along a number of directions. On one direction, we investigate which aspects, comprehensible by humans, hinder the CNNs while understanding a painting genre; subsequently by means of domain transfer, we retrieve information about the internal description and the organization of the painting clusters. In order to accomplish such a task, we annotate artistic photographic images with respect to the scene type related to genres and we stylize a large corpus of photographs using different style transfer methods. All this data will be made publicly available to be used in other research works.

On a second direction, this paper is the first to objectively evaluate the efficiency of the currently popular neural style transfer methods. Currently existing solutions [@Gatys:2015; @johnson2016; @ulyanov2016; @huang2017] compare themselves by speed, stability within video sequences or number of transferable styles. By quantifying the improvement while adapting photographs to the painting domain, we reach a surprising conclusion, namely that they are less or at most 