Discovering Shared Space for Visual Recognition by Cross-Domain Residual Learning

Discovering Shared Space for Visual Recognition by Cross-Domain Residual Learning | 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 Discovering Shared Space for Visual Recognition by Cross-Domain Residual Learning Jie Pan, Yufang Dan, Baoqi Zhao, Jianwen Tao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4568347/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 In order to solve the problem of inconsistent data distribution in machine learning, domain adaptation based on feature representation methods extract features from source domain, and transfer to target domain for classi cation. The existing feature representation based methods mainly solve the problem of inconsistent feature distribution between the source domain data and the target domain data, but only few methods analyze the correlation of cross-domain features between original space and shared latent space, which reduce the performance of domain adaptation. To this end, we propose a domain adaptation method with residual module, the main ideas of which are: (1) transfer the source domain data features to the target domain data through the shared latent space to achieve features sharing; (2) build a cross domain residual learning model using the latent feature space as the residual connection of the original feature space, which improves the propagation e ciency of features; (3) regular feature space to sparse features representation, which can improve the robustness of the model; and (4) give optimization algorithm, and the experiments on the public visual datasets verify the e ectiveness of the method. Shared space Residual model Domain adaptation Visual recognition 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. 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-4568347","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":319316749,"identity":"2f475cf0-5e9b-423b-8711-f9dc212d412d","order_by":0,"name":"Jie Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3PsWoCQRCA4TkOvGbAdoMkeYWVKxV8ldnGazS1RYqtNpWkvUAewi7YjRnQZrUWYpEq9UJ6idEmRdhsmWL/bmA+mAHI5f5h3bJkptnwBtR5Lv8mVw/OcPDj+kQKm0S09/Xqyb0am0xgT1qww81Lb755h9nA2GrLUVG0RIJ4mC6fd8aCb4zFO4qSUhELqo/pYj/p28LJ6ULUUdJRxgpqafSZHBMIosCqJaELsQlEVQ448Li/OOxMS+umdjiJk5F0P4M5Dm/125xDuB9cP1Y+Tn7eCEDf36XuX0gul8vlfusLRx9QH0uUbp8AAAAASUVORK5CYII=","orcid":"","institution":"Ningbo Polytechnic","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Pan","suffix":""},{"id":319316750,"identity":"5960aab3-7392-4d6d-9a36-98012c5e2c78","order_by":1,"name":"Yufang Dan","email":"","orcid":"","institution":"Ningbo Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Yufang","middleName":"","lastName":"Dan","suffix":""},{"id":319316751,"identity":"03e7d743-d6ac-4376-b8d2-ca026428c4ae","order_by":2,"name":"Baoqi Zhao","email":"","orcid":"","institution":"Ningbo Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Baoqi","middleName":"","lastName":"Zhao","suffix":""},{"id":319316752,"identity":"fea1d34f-ab6f-472f-9928-2522d6209344","order_by":3,"name":"Jianwen Tao","email":"","orcid":"","institution":"Ningbo Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Jianwen","middleName":"","lastName":"Tao","suffix":""}],"badges":[],"createdAt":"2024-06-12 07:37:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4568347/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4568347/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59795658,"identity":"39acf70f-235c-4684-84a1-e91ac5cd3ca7","added_by":"auto","created_at":"2024-07-07 10:01:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1926793,"visible":true,"origin":"","legend":"","description":"","filename":"lrda.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4568347/v1_covered_b7f1e04a-eb53-45c0-b1c9-95471e9a9749.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Discovering Shared Space for Visual Recognition by Cross-Domain Residual Learning","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":"Shared space, Residual model, Domain adaptation, Visual recognition","lastPublishedDoi":"10.21203/rs.3.rs-4568347/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4568347/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to solve the problem of inconsistent data distribution in machine learning, domain adaptation based on feature representation methods extract features from source domain, and transfer to target domain for classi cation. The existing feature representation based methods mainly solve the problem of inconsistent feature distribution between the source domain data and the target domain data, but only few methods analyze the correlation of cross-domain features between original space and shared latent space, which reduce the performance of domain adaptation. 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