in \[Sect:Results\].

For the domain adaptation function we investigate several alternatives. As a visual distinctive characteristic of painting is the style, the investigated functions are in the category of style transfer methods. As they are a key concept to our work, in the next subsection we will detail them.

Style transfer
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Following the seminal work of Reinhard et al. [-@Reinhard:2001] the idea to transfer elements from a reference image, with an artistic value, to a subject one increased in popularity. The main concepts introduced then still stand: first, both images are represented into a space that offers a meaningful description with respect to transferable elements; next, the subject image is altered such that it fits into the description of the reference image. Quite often the second step implies matching the estimated probability density functions (i.e histograms) of the two images. A simple version [-@Reinhard:2001; -@huang2017] is to assume the distribution in the feature space to be Gaussian, independent (or at least uncorrelated) with respect to the dimensions. Such an assumption is a sword with two edges: on one side, the feature space should be constrained to follow the decorrelation assumption, but, on the other hand, the matching process is 