{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105107"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105107","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Generative models for predictive UI design tools","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Situ, Jason Jun"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Kumar, Ranjitha"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:36:13Z","date_published":"2019-08-23T20:36:13Z","updated_at":"2026-07-22T22:24:44Z","subjects":["mobile design","generative models","gan","vae","design workflow"],"languages":["en"],"rights":["Copyright 2019 Jason Situ"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105107","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kumar, Ranjitha"]},{"key":"dc:creator","label":"Author","values":["Situ, Jason Jun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:36:13Z","2021-08-24T09:15:24Z","2019-04-26","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["mobile design","generative models","gan","vae","design workflow"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Jason Situ"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105107"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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. 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Finally, we present a mobile UI mockup tool that leverages the GAN-based model to support a predictive design workflow.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Jason Situ, accepted the attached license on 2019-04-25 at 21:57.","The student, Jason Situ, submitted this Thesis for approval on 2019-04-25 at 22:10.","This Thesis was approved for publication on 2019-04-26 at 11:13.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13938 on 2019-08-22 at 15:08:48","Made available in DSpace on 2019-08-23T20:36:13Z (GMT). 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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.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Jason Situ, accepted the attached license on 2019-04-25 at 21:57.","The student, Jason Situ, submitted this Thesis for approval on 2019-04-25 at 22:10.","This Thesis was approved for publication on 2019-04-26 at 11:13.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13938 on 2019-08-22 at 15:08:48","Made available in DSpace on 2019-08-23T20:36:13Z (GMT). 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