  We present interactive painting processes in which a painter and various
neural style transfer algorithms interact on a real canvas. Understanding what
these algorithms' outputs achieve is then paramount to describe the creative
agency in our interactive experiments. We gather a set of paired
painting-pictures images and present a new evaluation methodology based on the
predictivity of neural style transfer algorithms. We point some algorithms'
instabilities and show that they can be used to enlarge the diversity and
pleasing oddity of the images synthesized by the numerous existing neural style
transfer algorithms. This diversity of images was perceived as a source of
inspiration for human painters, portraying the machine as a computational
catalyst.
