A Hybrid Dropout Framework for Enhanced Generalization in Convolutional Neural Networks

A Hybrid Dropout Framework for Enhanced Generalization in Convolutional Neural Networks | 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 Short Report A Hybrid Dropout Framework for Enhanced Generalization in Convolutional Neural Networks Yashas Donthi, Talasila Dheeraj, Sahana S, Sravya D, Rajashree Shettar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8587657/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Regularization is a major challenge when training deep neu- ral networks, especially with small and medium-sized datasets. Tradi- tional dropout methods like standard dropout, Monte Carlo dropout, variational dropout, structured dropout, and concrete dropout aim to reduce overfitting. However, each method behaves inconsistently when used alone across different layers of the network. This paper presents a Hybrid Dropout Framework with a specific regularization approach for a specific dataset. For the CIFAR-10 dataset, the framework uses fixed- rate standard dropout in convolutional layers, adaptive concrete dropout in the dense layer, and structured dropout to improve spatial regulariza- tion. For the UCI Digits dataset, the hybrid approach combines standard dropout, concrete dropout, and Monte Carlo averaging during inference to improve the performance. The Monte Carlo averaging is applied dur- ing inference to predict uncertainty and avoid unnecessary computation overhead during the training phase. The framework is easy to implement and works with various architectures. The results obtained for applying this framework on both CIFAR-10 and UCI Digits dataset shows positive performance, highlighting the effectiveness irrespective of the size of the dataset. Using the proposed hybrid approach, this work achieves an ac- curacy of 90.5% on the CIFAR-10 dataset and 98.61% on the UCI Digits dataset. This shows improvements of 0.78% and 0.28%, respectively, in comparison to the best-performing individual dropout method. Hybrid dropout convolutional neural networks regular- ization concrete dropout Monte Carlo inference CIFAR10 UCI Digits Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-8587657","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":579440448,"identity":"ada6a0a3-334a-4930-ad1f-b2a4bc589b93","order_by":0,"name":"Yashas Donthi","email":"","orcid":"","institution":"RV College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Yashas","middleName":"","lastName":"Donthi","suffix":""},{"id":579440450,"identity":"74604b55-2878-4591-813f-e92ec96ce916","order_by":1,"name":"Talasila Dheeraj","email":"","orcid":"","institution":"RV College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Talasila","middleName":"","lastName":"Dheeraj","suffix":""},{"id":579440454,"identity":"13fd2663-a32e-44d4-a165-90c32665d55e","order_by":2,"name":"Sahana S","email":"","orcid":"","institution":"RV College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Sahana","middleName":"","lastName":"S","suffix":""},{"id":579440457,"identity":"2bf1a666-83ba-4b29-97d4-feba51b2f0cd","order_by":3,"name":"Sravya D","email":"","orcid":"","institution":"RV College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Sravya","middleName":"","lastName":"D","suffix":""},{"id":579440459,"identity":"292fee7c-0c69-4258-b52d-6929d8370725","order_by":4,"name":"Rajashree Shettar","email":"data:image/png;base64,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","orcid":"","institution":"RV College of Engineering","correspondingAuthor":true,"prefix":"","firstName":"Rajashree","middleName":"","lastName":"Shettar","suffix":""}],"badges":[],"createdAt":"2026-01-13 05:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8587657/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8587657/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103508260,"identity":"85c6bfe8-ee71-410e-991b-4791f06b73c4","added_by":"auto","created_at":"2026-02-26 13:47:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":851056,"visible":true,"origin":"","legend":"","description":"","filename":"HybridFrameworkDiscoverAI1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8587657/v1_covered_ce1b6baf-7cd2-4d81-8eb3-28da2b68a3da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Hybrid Dropout Framework for Enhanced Generalization in Convolutional Neural Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hybrid dropout, convolutional neural networks, regular- ization, concrete dropout, Monte Carlo inference, CIFAR10, UCI Digits","lastPublishedDoi":"10.21203/rs.3.rs-8587657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8587657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRegularization is a major challenge when training deep neu- ral networks, especially with small and medium-sized datasets. Tradi- tional dropout methods like standard dropout, Monte Carlo dropout, variational dropout, structured dropout, and concrete dropout aim to reduce overfitting. However, each method behaves inconsistently when used alone across different layers of the network. This paper presents a Hybrid Dropout Framework with a specific regularization approach for a specific dataset. For the CIFAR-10 dataset, the framework uses fixed- rate standard dropout in convolutional layers, adaptive concrete dropout in the dense layer, and structured dropout to improve spatial regulariza- tion. For the UCI Digits dataset, the hybrid approach combines standard dropout, concrete dropout, and Monte Carlo averaging during inference to improve the performance. The Monte Carlo averaging is applied dur- ing inference to predict uncertainty and avoid unnecessary computation overhead during the training phase. The framework is easy to implement and works with various architectures. 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