Transfer Learning is an important strategy in Computer Vision to tackle problems in the face of limited training data. However, this strategy still heavily depends on the amount of availabl data, which is a challenge for small heritage institutions. This paper investigates various
ways of enrichingsmaller digital heritage collections to boost the performance of deep learningmodels, using the identification of musical instruments as a case study. We apply traditional data augmentation techniques as well as the use of an external, photorealistic collection, distorted
by Style Transfer. Style Transfer techniques are capable of artistically stylizing images, reusing the style from any other given image. Hence, collections can be easily augmented with artificially generated images. We introduce the distinction between inner and outer style transfer and show
that artificially augmented images in both scenarios consistently improve classification results, on top of traditional data augmentation techniques. However, and counter-intuitively, such artificially generated artistic depictions of works are surprisingly hard to classify. In addition, we
discuss an example of negative transfer within the non-photorealistic domain.