[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122226-en":3,"doc-seo-122226-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122226,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Generated Image Quality Assurance and Generative Adversarial Network Augmentation for Machine Learning - Thesis","Thesis focused on improving machine learning outputs through two connected components: image quality assurance and data augmentation using a Generative Adversarial Network (GAN). The work positions quality control as a practical need for reliable generated imagery, while GAN-based augmentation aims to expand training data and strengthen model performance. Copyright, moral rights, and reuse terms outline how the thesis and any associated data may be accessed and redistributed under the stated license conditions, including restrictions for third-party materials and guidance for permissions when required.","School of Engineering, Computing and Mathematics Theses Faculty of Science and Engineering Theses  \n2025  \nGenerated Image Quality Assurance and Generative Adversarial Network Augmentation for Machine Learning  \nDirk Holscher School of Engineering, Computing and Mathematics  \nLet us know how access to this document benefits you  \nThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License  \nGeneral rights  \nAll content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author. Take down policy  \nIf you believe that this document breaches copyright please contact the library providing details, and we will remove access to the work immediately and investigate your claim.  \nFollow this and additional works at: [https://pearl.plymouth.ac.uk/secam-theses](https://pearl.plymouth.ac.uk/secam-theses)  \nRecommended Citation  \nHolscher, D. (2025) Generated Image Quality Assurance and Generative Adversarial Network Augmentation for Machine Learning. Thesis. University of Plymouth. Retrieved from  \n[https://pearl.plymouth.ac.uk/secam-theses/551](https://pearl.plymouth.ac.uk/secam-theses/551)  \nThis Thesis is brought to you for free and open access by the Faculty of Science and Engineering Theses at PEARL. It has been accepted for inclusion in School of Engineering, Computing and Mathematics Theses by an authorized administrator of PEARL. For more information, [please contact openresearch@plymouth.ac.uk](please contact openresearch@plymouth.ac.uk).  \nPEARL  \nPHD  \nGenerated Image Quality Assurance and Generative Adversarial Network Augmentation for Machine Learning  \nHolscher, Dirk  \nAward date:  \n2025  \nLink to publication in PEARL  \nAll content in PEARL is protected by copyright law.  \nThe author assigns certain rights to the University of Plymouth including the right to make the thesis accessible and discoverable via the British Library’s Electronic Thesis Online Service (EThOS) and the University research repository (PEARL), and to undertake activities to migrate, preserve and maintain the medium, format and integrity of the deposited file for future discovery and use.  \nCopyright and Moral rights arising from original work in this thesis and (where relevant), any accompanying data, rests with the Author unless stated otherwise* .  \nRe-use of the work is allowed under fair dealing exceptions outlined in the Copyright, Designs and Patents Act 1988 (amended), and the terms of the copyright licence assigned to the thesis by the Author.  \nIn practice, and unless the copyright licence assigned by the author allows for more permissive use, this means,  \nThat any content or accompanying data cannot be extensively quoted, reproduced or changed without the written permission of the author / rights holder  \nThat the work in whole or part may not be sold commercially in any format or medium without the written permission of the author / rights holder  \n* Any third-party copyright material in this thesis remains the property of the original owner. Such third-party copyright work included in the thesis will be clearly marked and attributed, and the original licence under which it was released will be specified . This material is not covered by the licence or terms assigned to the wider thesis and must be used in accordance with the original licence; or separate permission must be sought from the copyright holder.  \nDownload date: 23. Apr. 2025  \nPEARL  \nPHD  \nGenerated Image Quality Assurance and Generative Adversarial Network Augmentation for Machine Learning  \nHolscher, Dirk  \nAward date:  \n2025  \nLink to publication in PEARL  \nAll content in PEARL is protected by copyright law.  \nThe author assigns certain rig","cbCaibnUw51SNc1w","https://ap.wps.com/l/cbCaibnUw51SNc1w","pdf",35880688,1,185,"English","en",105,"# Overview and Metadata\n## Publication Details and Citation\n## Copyright and Reuse Conditions\n## Download Information","[{\"question\":\"What is the thesis about?\",\"answer\":\"The thesis investigates image quality assurance together with GAN-based augmentation to support machine learning.\"},{\"question\":\"Who is the author and where was the thesis published?\",\"answer\":\"The author is Dirk Holscher, and the thesis is hosted in the University of Plymouth’s PEARL repository.\"},{\"question\":\"Under what license can the thesis be reused?\",\"answer\":\"Reuse is governed by the Creative Commons Attribution-NonCommercial 4.0 International License, with additional conditions for fair dealing and third-party materials.\"}]","Generated Image Quality Assurance and Generative Adversarial Network Augmentation for Machine Learning - 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