  We introduce a new technique that automatically generates diverse, visually
compelling stylizations for a photograph in an unsupervised manner. We achieve
this by learning style ranking for a given input using a large photo collection
and selecting a diverse subset of matching styles for final style transfer. We
also propose a novel technique that transfers the global color and tone of the
chosen exemplars to the input photograph while avoiding the common visual
artifacts produced by the existing style transfer methods. Together, our style
selection and transfer techniques produce compelling, artifact-free results on
a wide range of input photographs, and a user study shows that our results are
preferred over other techniques.
