higher performance than AlexNet. Although we do not use this network for direct classification, the stacked convolutional layers of VGG–-19 trained on ImageNet produce very efficient feature descriptors, and, thus, are heavily used by the style transfer algorithms [@Gatys:2015; @johnson2016].

In the remainder of the paper, for the task of classification, we will use the Residual Network (ResNet) [@He2016] architecture, with 34 layers. All the hyper-parameters and the training procedure follow precisely the original ResNet [@He2016]. Nominally, the optimization algorithm is 1–bit Stochastic Gradient Descent, the initialization is random (i.e. from scratch) and when the recognition accuracy levels on the validation set, we decrease the learning rate by a factor of 10. The implementation is based on the CNTK library[^3].

Painting Understanding and Domain Transfer {#Sect:PaintingUnderstanding}
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The main task of this work is to generate results about the understanding of the machine learning systems (in our case deep CNN) grasp of art. In such a case, one needs tools to ease the comprehension of the system internal mechanisms.

For CNNs, the most popular visualization tool has been proposed by Zeiler and Fergus [-@Zeiler:14] by introducing deconvolutional layers and visualizing activations maps onto features. Attempts to visualize the CNNs, for 