reachable if the network parameters are previously trained for a different task on a large database, such as ImageNet. Next, these values are updated to the given task. This is called fine–tuning and it is a case of transfer learning. As our investigation is related to a different domain transfer, we will avoid to use both of them simultaneuosly, in order to establish clearer conclusions. To compensate, we are relying on the recent architecture of the Residual Networks (Resnet [@He2016]) that was shown to be able to overcome the problem of vanishing gradients, reaching better accuracy for the same number of parameters, when compared to previous architectures.

Contribution and paper organization
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This paper extends our previous works [@florea2017:Scia; @badea2017], being mostly developed from [@florea2017:Scia], where we had initiated the discussion about the efficiency of various methods to transfer information from the photographic domain to the paintings domain, such that the recognition by CNNs’ of paintings genre is improved. In this paper we significantly extend the discussion, by including other transfer methods and by adding more significant results that allow crisper conclusions. In the second work ([@badea2017]), we showed that the artistic style transfer remains as efficient even if a 