A Concept-Aware Explainability Method for Convolutional Neural Networks

A Concept-Aware Explainability Method for 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 Research Article A Concept-Aware Explainability Method for Convolutional Neural Networks Mustafa Kagan Gurkan, Fatos Yarman Vural, Nafiz Arica This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3973292/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jan, 2025 Read the published version in Machine Vision and Applications → Version 1 posted 9 You are reading this latest preprint version Abstract Although Convolutional Neural Networks (CNN) outperform the classical models in a wide range of Machine Vision applications, their restricted interpretability and their lack of comprehensibility inreasoning, generates many problems such as security, reliability and safety. Consequently, there is a growing need on research to improve explainability and address their limitations. In this paper, we propose a concept-based method, called Concept-Aware Explainability (CAE) to provide a verbal explanation for the predictions of pre-trained CNN models. A new measure, called detection score mean, is introduced to quantify the relationship between the filters of the model and a set of pre-defined concepts. Based on the detection score mean values, we define sorted lists of Concept-Aware Filters (CAF) and Filter-Activating Concepts (FAC). These lists are used to generate explainability reports, where we can explain, analyze, and compare models in terms of the concepts embedded in the image. The proposed explainability method is compared to the state-of-the-art methods to explain Resnet18 and VGG16 models, pre-trained on ImageNet and Places365-Standard datasets. Two popular metrics, namely, number of unique detectors and numberof detecting filters, are used to make a quantitative comparison. Superior performances are observed for the suggested CAE, when compared to Network Dissection (NetDis) [1] and Net2Vec [2] methods. Convolutional Neural Networks Concept-based Explanation Filter-Concept Association Model Comparison via Explanations Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Jan, 2025 Read the published version in Machine Vision and Applications → Version 1 posted Editorial decision: Revision requested 23 Sep, 2024 Reviews received at journal 23 Sep, 2024 Reviews received at journal 28 Jul, 2024 Reviewers agreed at journal 07 Jul, 2024 Reviewers agreed at journal 05 Jul, 2024 Reviewers invited by journal 22 Apr, 2024 Submission checks completed at journal 21 Feb, 2024 Editor assigned by journal 21 Feb, 2024 First submitted to journal 20 Feb, 2024 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-3973292","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274025540,"identity":"f1a5022b-0f45-4eaa-8fe3-65286e5bf835","order_by":0,"name":"Mustafa Kagan Gurkan","email":"data:image/png;base64,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","orcid":"","institution":"Bahçeşehir University","correspondingAuthor":true,"prefix":"","firstName":"Mustafa","middleName":"Kagan","lastName":"Gurkan","suffix":""},{"id":274025541,"identity":"aa42de42-950b-42d1-9d5d-98ec4eea0c43","order_by":1,"name":"Fatos Yarman Vural","email":"","orcid":"","institution":"Middle East Technical University","correspondingAuthor":false,"prefix":"","firstName":"Fatos","middleName":"Yarman","lastName":"Vural","suffix":""},{"id":274025542,"identity":"00dbcc29-4acd-445a-b494-9e362d3f5cfa","order_by":2,"name":"Nafiz Arica","email":"","orcid":"","institution":"Piri Reis University","correspondingAuthor":false,"prefix":"","firstName":"Nafiz","middleName":"","lastName":"Arica","suffix":""}],"badges":[],"createdAt":"2024-02-20 16:16:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3973292/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3973292/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00138-024-01653-w","type":"published","date":"2025-01-11T15:56:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73693799,"identity":"47e3ede5-6432-4fb4-83fe-2d32780572eb","added_by":"auto","created_at":"2025-01-13 16:06:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2258151,"visible":true,"origin":"","legend":"","description":"","filename":"CAEforCNNs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3973292/v1_covered_11d56275-8331-41ae-8c9f-367c8b7b0b02.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Concept-Aware Explainability Method for Convolutional Neural Networks","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":"machine-vision-and-applications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mvap","sideBox":"Learn more about [Machine Vision and Applications](https://www.springer.com/journal/138)","snPcode":"138","submissionUrl":"https://submission.springernature.com/new-submission/138/3","title":"Machine Vision and Applications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Convolutional Neural Networks, Concept-based Explanation, Filter-Concept Association, Model Comparison via Explanations","lastPublishedDoi":"10.21203/rs.3.rs-3973292/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3973292/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Although Convolutional Neural Networks (CNN) outperform the classical models in a wide range of Machine Vision applications, their restricted interpretability and their lack of comprehensibility inreasoning, generates many problems such as security, reliability and safety. 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