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

Year 2021
Abstract URL view
ISBN-13 9781450380188

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.

Details

IUI '21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, Companion. ACM 2021, ISBN 978-1-4503-8018-8

Authors

  • Nishit Gajjar
  • Vinoth Pandian Sermuga Pandian
  • Sarah Suleri
  • Matthias Jarke

Presented at

26th International Conference on Intelligent User Interfaces, 2021 , College Station, Texas , US.

Published in

26th International Conference on Intelligent User Interfaces , by Tracy Hammond, Katrien Verbert, Dennis Parra , p. 40-42 ; ACM .

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