  Style analysis of artwork in computer vision predominantly focuses on
achieving results in target image generation through optimizing understanding
of low level style characteristics such as brush strokes. However,
fundamentally different techniques are required to computationally understand
and control qualities of art which incorporate higher level style
characteristics. We study style representations learned by neural network
architectures incorporating these higher level characteristics. We find
variation in learned style features from incorporating triplets annotated by
art historians as supervision for style similarity. Networks leveraging
statistical priors or pretrained on photo collections such as ImageNet can also
derive useful visual representations of artwork. We align the impact of these
expert human knowledge, statistical, and photo realism priors on style
representations with art historical research and use these representations to
perform zero-shot classification of artists. To facilitate this work, we also
present the first large-scale dataset of portraits prepared for computational
analysis.
