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Title:Generative models for predictive UI design tools
Author(s):Situ, Jason Jun
Advisor(s):Kumar, Ranjitha
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):mobile design
generative models
gan
vae
design workflow
Abstract:User interface (UI) design is a central part of the mobile app creation process, which involves specifying the elements that should be placed on a screen, and how they should be arranged and styled. This paper introduces a generative model approach to predictive design for mobile UI layouts. Given a partial UI design, the model predicts the next UI element that should be added to the layout. Moreover, the model can be used queried multiple times in succession to autocomplete an entire UI screen. To power this design interaction, we present two types of models: generative adversarial networks (GANs) [7] and variational auto-encoders (VAEs) [15]. We train the GAN and VAE models over 1949 mobile UIs that represent a variety of screen types (e.g. Login, Onboarding), and compare both models along standard and design-based metrics, identifying key tradeoffs. Finally, we present a mobile UI mockup tool that leverages the GAN-based model to support a predictive design workflow.
Issue Date:2019-04-26
Type:Text
URI:http://hdl.handle.net/2142/105107
Rights Information:Copyright 2019 Jason Situ
Date Available in IDEALS:2019-08-23
Date Deposited:2019-05


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