  Machine-learning excels in many areas with well-defined goals. However, a
clear goal is usually not available in art forms, such as photography. The
success of a photograph is measured by its aesthetic value, a very subjective
concept. This adds to the challenge for a machine learning approach.
  We introduce Creatism, a deep-learning system for artistic content creation.
In our system, we break down aesthetics into multiple aspects, each can be
learned individually from a shared dataset of professional examples. Each
aspect corresponds to an image operation that can be optimized efficiently. A
novel editing tool, dramatic mask, is introduced as one operation that improves
dramatic lighting for a photo. Our training does not require a dataset with
before/after image pairs, or any additional labels to indicate different
aspects in aesthetics.
  Using our system, we mimic the workflow of a landscape photographer, from
framing for the best composition to carrying out various post-processing
operations. The environment for our virtual photographer is simulated by a
collection of panorama images from Google Street View. We design a
"Turing-test"-like experiment to objectively measure quality of its creations,
where professional photographers rate a mixture of photographs from different
sources blindly. Experiments show that a portion of our robot's creation can be
confused with professional work.
