  On a constant quest for inspiration, designers can become more effective with
tools that facilitate their creative process and let them overcome design
fixation. This paper explores the practicality of applying neural style
transfer as an emerging design tool for generating creative digital content. To
this aim, the present work explores a well-documented neural style transfer
algorithm (Johnson 2016) in four experiments on four relevant visual
parameters: number of iterations, learning rate, total variation, content vs.
style weight. The results allow a pragmatic recommendation of parameter
configuration (number of iterations: 200 to 300, learning rate: 2e-1 to 4e-1,
total variation: 1e-4 to 1e-8, content weights vs. style weights: 50:100 to
200:100) that saves extensive experimentation time and lowers the technical
entry barrier. With this rule-of-thumb insight, visual designers can
effectively apply deep learning to create artistic visual variations of digital
content. This could enable designers to leverage AI for creating design works
as state-of-the-art.
