Interactive Neural Style Transfer with Artists
This work addresses the challenge of creative agency in human-machine collaboration for artists, though it is incremental in evaluating existing algorithms.
The paper tackled the problem of understanding neural style transfer algorithms' outputs in interactive painting processes, showing that algorithm instabilities can increase image diversity and serve as inspiration for human painters.
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.