Deep Transfer Learning for Art Classification Problems

In this paper we investigate whether Deep Convolutional Neural Net-
works (DCNNs), which have obtained state of the art results on the ImageNet
challenge, are able to perform equally well on three different art classification
problems. In particular, we assess whether it is beneficial to fine tune the net-
works instead of just using them as off the shelf feature extractors for a sepa-
rately trained softmax classifier. Our experiments show how the first approach
yields significantly better results and allows the DCNNs to develop new selective
attention mechanisms over the images, which provide powerful insights about
which pixel regions allow the networks successfully tackle the proposed classi-
fication challenges. Furthermore, we also show how DCNNs, which have been
fine tuned on a large artistic collection, outperform the same architectures which
are pre-trained on the ImageNet dataset only, when it comes to the classification
of heritage objects from a different dataset.