Soft Margin Spectral Normalization for GANs

Soft Margin Spectral Normalization for GANs | 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Article Soft Margin Spectral Normalization for GANs Alexander Rogachev, Fedor Ratnikov This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3715727/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract In this paper, we explore the use of Generative Adversarial Networks (GANs) to speed up the simulation process while ensuring that the generated results are consistent in terms of physics metrics. Our main focus is the application of spectral normalization for GANs to generate electromagnetic calorimeter (ECAL) response data, which is a crucial component of the LHCb. We propose an approach that allows to balance between model's capacity and stability during training procedure, compare it with previously published ones and study the relationship between proposed method's hyperparameters and quality of generated objects. We show that the tuning of normalization method's hyperparameters boosts the quality of generative model. Generative Models High Energy Physics Deep Learning Regularization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Mar, 2024 Reviews received at journal 11 Jan, 2024 Reviewers agreed at journal 25 Dec, 2023 Reviewers invited by journal 25 Dec, 2023 Editor assigned by journal 18 Dec, 2023 Submission checks completed at journal 08 Dec, 2023 First submitted to journal 06 Dec, 2023 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-3715727","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":257471260,"identity":"f0d31327-edd9-4506-82b8-c670002249ca","order_by":0,"name":"Alexander Rogachev","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYDACZgiVAGQdI1kLWxpplgG18JgRp5S/nffhwy81dnn8EjnfHv6oYMg3OEBAi8RhdmNjmWPJxZIzcrcb85xhsNxASAvDYTY2aQm2A4kbzpzdJs3YxmBA0BZ5sJZ/BxL3nznzTPLnPyK0GAC1SH5sA9rC3sMmwdtAhBbDw2zMxox9yYkzjreZSfMckzCQJKRF7vwxxoc/vtkl9jczP5P8UWNjwEdICwgw8yDYEkSoBwLGH8SpGwWjYBSMgpEKAE4dPOZOnK/OAAAAAElFTkSuQmCC","orcid":"","institution":"HSE University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Rogachev","suffix":""},{"id":257471262,"identity":"3a04e7a0-a300-461e-a3ca-7eb33cf941a1","order_by":1,"name":"Fedor Ratnikov","email":"","orcid":"","institution":"HSE University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fedor","middleName":"","lastName":"Ratnikov","suffix":""}],"badges":[],"createdAt":"2023-12-06 15:14:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3715727/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3715727/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":47989602,"identity":"ba3b744e-b948-436c-82bb-bbf44ce8e4c7","added_by":"auto","created_at":"2023-12-11 15:10:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":346623,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplateSoftMarginSpectralNormalizationforGANs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3715727/v1_covered_54b73608-256c-4ef1-b8ee-e612bd792671.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Soft Margin Spectral Normalization for GANs","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":"computing-and-software-for-big-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"csbs","sideBox":"Learn more about [Computing and Software for Big Science](http://link.springer.com/journal/41781)","snPcode":"41781","submissionUrl":"https://submission.nature.com/new-submission/41781/3","title":"Computing and Software for Big Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Generative Models, High Energy Physics, Deep Learning, Regularization","lastPublishedDoi":"10.21203/rs.3.rs-3715727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3715727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In this paper, we explore the use of Generative Adversarial Networks (GANs) to speed up the simulation process while ensuring that the generated results are consistent in terms of physics metrics. Our main focus is the application of spectral normalization for GANs to generate electromagnetic calorimeter (ECAL) response data, which is a crucial component of the LHCb. We propose an approach that allows to balance between model's capacity and stability during training procedure, compare it with previously published ones and study the relationship between proposed method's hyperparameters and quality of generated objects. We show that the tuning of normalization method's hyperparameters boosts the quality of generative model.","manuscriptTitle":"Soft Margin Spectral Normalization for GANs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-11 15:04:48","doi":"10.21203/rs.3.rs-3715727/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-22T14:57:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-01-12T03:27:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"00fdc58a-3168-4cc8-b1c0-4daec79281e4","date":"2023-12-25T22:44:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-12-25T19:06:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-18T13:01:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-12-08T10:30:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Computing and Software for Big Science","date":"2023-12-06T15:10:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"computing-and-software-for-big-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"csbs","sideBox":"Learn more about [Computing and Software for Big Science](http://link.springer.com/journal/41781)","snPcode":"41781","submissionUrl":"https://submission.nature.com/new-submission/41781/3","title":"Computing and Software for Big Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d4810d78-197c-4b8f-b3a8-0409bbd5a6be","owner":[],"postedDate":"December 11th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-04T05:11:37+00:00","versionOfRecord":[],"versionCreatedAt":"2023-12-11 15:04:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3715727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3715727","identity":"rs-3715727","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}