  The diversity of painting styles represents a rich visual vocabulary for the
construction of an image. The degree to which one may learn and parsimoniously
capture this visual vocabulary measures our understanding of the higher level
features of paintings, if not images in general. In this work we investigate
the construction of a single, scalable deep network that can parsimoniously
capture the artistic style of a diversity of paintings. We demonstrate that
such a network generalizes across a diversity of artistic styles by reducing a
painting to a point in an embedding space. Importantly, this model permits a
user to explore new painting styles by arbitrarily combining the styles learned
from individual paintings. We hope that this work provides a useful step
towards building rich models of paintings and offers a window on to the
structure of the learned representation of artistic style.

Computer Vision and Pattern Recognition;Machine Learning