GAN-Prototype: Generative Adversarial Networks for Rapid UX Prototype Generation

GAN-Prototype: Generative Adversarial Networks for Rapid UX Prototype Generation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article GAN-Prototype: Generative Adversarial Networks for Rapid UX Prototype Generation Songhang Deng, Xiang Li, Hang Wang, Xiaolan Ke, Ruilin Nong, Xingzu Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7440802/v3 This work is licensed under a CC BY 4.0 License Status: Posted Version 3 posted You are reading this latest preprint version Show more versions Abstract Generative Adversarial Networks (GANs) have shown remarkable potential in various domains, and their application in user experience (UX) design offers a transformative approach. In this paper, we present GAN-Prototype, a novel framework designed to automate UX prototype generation. By employing an adversarial model, GAN-Prototype consists of a generator that creates realistic prototypes grounded on specified design parameters, while a discriminator assesses these designs for authenticity and usability. This dual mechanism allows for rapid development of high-fidelity prototypes, significantly reducing the time typically needed in conventional design practices. The framework's learning process utilizes a rich dataset of existing UX designs, capturing the complex interplay between design features and user preferences. Additionally, incorporating user feedback mechanisms facilitates iterative improvements to prototypes based on actual user interactions. Experimental results confirm that GAN-Prototype accelerates the design process without compromising user satisfaction, evidencing its role in enhancing efficiency and innovation within UX design. Practical case studies further illustrate the framework's applicability across diverse design scenarios, affirming its potential impact on designers' productivity and creativity. Computer Architecture and Engineering Diffusion Model High-fidelity Prototypes Generative UX Design Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 3 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7440802","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":520068309,"identity":"5183ba9d-21d0-47fa-a05b-e38387493da2","order_by":0,"name":"Songhang 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