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.
@article{arxiv.1708.00684,
title = {OmniArt: Multi-task Deep Learning for Artistic Data Analysis},
author = {Gjorgji Strezoski and Marcel Worring},
journal= {arXiv preprint arXiv:1708.00684},
year = {2017}
}