  Vast amounts of artistic data is scattered on-line from both museums and art
applications. Collecting, processing and studying it with respect to all
accompanying attributes is an expensive process. With a motivation to speed up
and improve the quality of categorical analysis in the artistic domain, in this
paper we propose an efficient and accurate method for multi-task learning with
a shared representation applied in the artistic domain. We continue to show how
different multi-task configurations of our method behave on artistic data and
outperform handcrafted feature approaches as well as convolutional neural
networks. In addition to the method and analysis, we propose a challenge like
nature to the new aggregated data set with almost half a million samples and
structured meta-data to encourage further research and societal engagement.
