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

by   Chaehan So, et al.

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.


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