Deep Learning Model Optimization in Creative Generation for New Media Animated Ads

Deep Learning Model Optimization in Creative Generation for New Media Animated Ads | 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 Deep Learning Model Optimization in Creative Generation for New Media Animated Ads Manlu Kong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5879017/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jun, 2025 Read the published version in Discover Artificial Intelligence → Version 1 posted 7 You are reading this latest preprint version Abstract This study delves into the application of deep learning models based on improved Generative Adversarial Networks (GANs) and Variable Auto-Encoders (VAEs) for creative generation of animated advertisements in new media. By constructing an effective optimization model framework, the study significantly improves the visual quality, creative diversity and user engagement of advertisement images. The experimental results show that the improved GANs (V-GANs) outperform the traditional generation models, such as Vanilla GAN, VAE and CGAN, in several aspects, including visual quality, creative diversity and user engagement.The PSNR and SSIM metrics of the V-GANs reach 33.5 dB and 0.92, respectively, in generating advertisement images, which shows its detail preservation and realism presentation advantages. In addition, V-GANs also perform well in terms of creative diversity, and the ad images they generate are more innovative and unique. In the user engagement evaluation, the interaction rate of V-GANs is as high as 14.8%, indicating that their generated advertisements are more capable of attracting users' attention and engagement. The improved V-GANs model will help to promote the further development of new media ad creative generation technology and provide more powerful and intelligent solutions for brand marketing. Deep Learning Generative Adversarial Networks (GANs) Variational Autoencoders (VAEs) New Media Advertising Creative Generation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Jun, 2025 Read the published version in Discover Artificial Intelligence → Version 1 posted Editorial decision: Accepted 27 May, 2025 Reviews received at journal 09 May, 2025 Reviewers agreed at journal 29 Apr, 2025 Editor assigned by journal 28 Apr, 2025 Reviewers invited by journal 16 Apr, 2025 Submission checks completed at journal 16 Apr, 2025 First submitted to journal 09 Apr, 2025 You are reading this latest preprint version 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. 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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-5879017","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443806046,"identity":"06543673-9a0c-461d-a2c7-c21bbab1a569","order_by":0,"name":"Manlu Kong","email":"data:image/png;base64,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","orcid":"","institution":"Anhui Wenda College of Information Engineering","correspondingAuthor":true,"prefix":"","firstName":"Manlu","middleName":"","lastName":"Kong","suffix":""}],"badges":[],"createdAt":"2025-01-22 08:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5879017/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5879017/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s44163-025-00354-1","type":"published","date":"2025-06-09T15:57:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84726522,"identity":"42ff1faa-3bb6-46d9-8b64-4c7a8eec3ef6","added_by":"auto","created_at":"2025-06-16 16:06:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1072249,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5879017/v1_covered_76721ccc-13ae-4a11-87ff-79a7de3125e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning Model Optimization in Creative Generation for New Media Animated Ads","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep Learning, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), New Media Advertising, Creative Generation","lastPublishedDoi":"10.21203/rs.3.rs-5879017/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5879017/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study delves into the application of deep learning models based on improved Generative Adversarial Networks (GANs) and Variable Auto-Encoders (VAEs) for creative generation of animated advertisements in new media. 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