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On a constant quest for inspiration, designers can become more effective withtools that facilitate their creative process and let them overcome designfixation. This paper explores the practicality of applying neural styletransfer as an emerging design tool for generating creative digital content. Tothis aim, the present work explores a well-documented neural style transferalgorithm (Johnson 2016) in four experiments on four relevant visualparameters: number of iterations, learning rate, total variation, content vs.style weight. The results allow a pragmatic recommendation of parameterconfiguration (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 to200:100) that saves extensive experimentation time and lowers the technicalentry barrier. With this rule-of-thumb insight, visual designers caneffectively apply deep learning to create artistic visual variations of digitalcontent. This could enable designers to leverage AI for creating design worksas state-of-the-art.

A Pragmatic AI Approach to Creating Artistic Visual Variations by Neural Style Transfer