has been pre-trained on another database, and, with a small learning rate, adapting it to the current task.

In contrast, the concept of domain transfer or domain adaptation appeared as an alternative to the increase of the amount of information over which a learner may be trained directly (without fine tuning) in order to improve its prediction capabilities. Many previous solutions and alternatives have been introduced. We will refer to the work of Ben-David et al. [-@Ben2010] for theoretical insights on the process.

It has been shown that domain transfer is feasible and the resulting learner has improved performance if the two domains are adapted. In this context one needs to mention the work of Saenko et al. [-@Saenko2010] that proved that using a trained transformation, the domain transfer is beneficial.

In our case, the target domain is the artistic painting domain, while for the source domain we consider several alternatives: consumer photographic image domain (represented by images from the SUN database), artistic photographic image domain (with instances from the Photo-Artist database), and a combination of features from the painting domain (taken from the “older”, less abstract styles of paintings); these will be detailed further in section \[Sect:Databases\] and, respectively, 