related to the scene, while the genre is hard to be defined. For artistic photos, the “genre” gets meaning again.

Starting from the idea that Deep Neural Networks share similarities with the human vision [@Cichy16] and the fact that such networks are already proven to be efficient in other perception-inspired areas, like object recognition or even in creating artistic images, we ask ourselves if they can pass the abstraction limit of artistic paintings and correctly recognize the scene type of such a work.

In this paper we will first work with the Residual Network (ResNet) on the standard WikiArt database so to obtain state of the art results. Afterwards, we will test different domain transfer augmentations to see if they can increase the recognition rate; also we will study if the network is capable to pass the abstraction limit and learn from different types of images that contain the same type of scenes. Furthermore, we introduce several alternatives for domain transfer to achieve a dual-task: improve the scene recognition performance and understand the abstraction capabilities of machine learning systems.

Regarding deep networks, multiple improvements have been proposed. In many situations, if the given task database is small, better performance is 