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Akin: Generating UI Wireframes from UI Design Patterns Using Deep Learning

: Gajjar, Nishit; Pandian, Vinoth Pandian Sermuga; Suleri, Sarah; Jarke, Matthias


Verbert, K. ; Association for Computing Machinery -ACM-:
IUI 2021, 26th International Conference on Intelligent User Interfaces : College Station, Texas, USA, April 14 - 17, 2021, Virtually
New York: ACM, 2021
ISBN: 978-1-4503-8018-8
International Conference on Intelligent User Interfaces (IUI) <26, 2021, Online>
Fraunhofer FIT ()

During the User interface (UI) design process, designers use UI design patterns for conceptualizing different UI wireframes for an application. This paper introduces Akin, a UI wireframe generator that allows designers to chose a UI design pattern and provides them with multiple UI wireframes for a given UI design pattern. Akin uses a fine-tuned Self-Attention Generative Adversarial Network trained with 500 UI wireframes of 5 android UI design patterns. Upon evaluation, Akin’s generative model provides an Inception Score of 1.63 (SD=0.34) and Fréchet Inception Distance of 297.19. We further conducted user studies with 15 UI/UX designers to evaluate the quality of Akin-generated UI wireframes. The results show that UI/UX designers considered wireframes generated by Akin are as good as wireframes made by designers. Moreover, designers identified Akin-generated wireframes as designer-made 50% of the time. This paper provides a baseline for further research in UI wireframe generation by providing a baseline metric.