https://m2.mtmt.hu/ hun 2026-05-31 00:51 BookChapter 34608877 APPROVED true 0 false 2025-09-01T07:24:31.467+0000 2024-02-29T07:28:47.993+0000 2024-02-17T11:38:57.971+0000 true 10094016 Szécsényi Nándor /api/author/10094016 Author true 2024-02-29T07:28:48.187+0000 2024-02-29T07:28:48.187+0000 true false true 25 25 /api/publicationtype/25 PublicationType 2 true 25 BookChapter true 10000312 Conference paper true 25 25 /api/publicationtype/25 PublicationType 2 true 25 BookChapter /api/subtype/10000312 Konferenciaközlemény SubType 228 true 10000312 true 1 /api/category/1 Category true 1 Nándor, Szécsényi Efficient Annotation Methods for Industrial Datasets using Artificial Intelligence 64 75 64 12 2023 Due to the rapid development and growth of Artificial Intelligence in recent times, it is also being used more and more frequently in industrial environments, however there are many problems besides its advantages. One of them is the difficult process of Dataset construction which often involves manual sample annotation. This task is often performed by human labor at larger companies with relative ease, smaller projects or developments however usually lack the necessary resources to do so, thus making it seem advantageous to use Artificial Intelligence, more precisely Deep Learning, for this problem too. Another issue is the asymmetry of these Datasets, as with production lines, the chance of failure is kept very low, resulting in only a few error samples. To overcome these issues and further democratize AI, in this paper, we present 2 latent space-based sampling methods, which based on the acquired results can achieve reasonably good classification performance with only annotating a few 100s of samples. Moreover, to aid in the understanding of the results, the presented techniques are compared to a naive method representing the baseline approach. true 2024 false true true true true false NONE 2025-09-01 false 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 false false false false 2024-05-17T11:32:33.856+0000 Elektrotechnika Csoport (BME / VIK / AAIT) 1 6 1 1 0 true true 10002 English /api/language/10002 Angol Language true 2 false true 10003 true 10001 /api/country/10001 Country true 1 /api/city/10003 City true 10005024 true 24127723 /api/publicationidentifier/24127723 https://www.worldcat.org/search?q=isbn%3A9789634219262 9789634219262 PublicationIdentifier true 122 ISBN https://www.worldcat.org/search?q=isbn%3A@@@ /api/publicationsource/122 ISBN PlainSource true true 10002 /api/publicationsourcetype/10002 PublicationSourceType true false 122 true false /api/publication/34051102 true true 23 23 /api/publicationtype/23 PublicationType 3 true 23 Book Proceedings of the Automation and Applied Computer Science Workshop 2023 (AACS'23) false true 34051102 true false true true true 10000144 Conference proceedings true 23 23 /api/publicationtype/23 PublicationType 3 true 23 Book /api/subtype/10000144 Konferenciakötet SubType 345 true 10000144 false Book 2023 true 1 /api/category/1 Category true 1 false Language 10002 /api/language/10002 Angol English true 2 true PersonAuthorship 114654974 /api/authorship/114654974 1 1.0 true false Author 10094016 /api/author/10094016 Szécsényi Nándor true true Nándor Szécsényi true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true Keyword 1880938 /api/keyword/1880938 true true Keyword 3116120 /api/keyword/3116120 true true Keyword 3253744 /api/keyword/3253744 true true /api/publication/34608877 <div class="BookChapter Publication long-list"> <div class="authors"> <img title="Forrásközlemény" style="float: left" src="/frontend/resources/grid/publication-core-icon.png"> <div class="autype autype0"> <span class="author-name" mtid="10094016"><a href="/gui2/?type=authors&mode=browse&sel=10094016" target="_blank">Nándor Szécsényi (<span class="authorship-author-name">Szécsényi Nándor</span> <span class="authorAux-mtmt"> mesterséges intelligencia alkalmazása teljesítm...</span>) </a> </span> <span class="author-affil"><span title="Budapesti Műszaki és Gazdaságtudományi Egyetem">BME</span>/<span title="Villamosmérnöki és Informatikai Kar">VIK</span>/<span title="Automatizálási és Alkalmazott Informatikai Tanszék">AAIT</span>/Elektrotechnika Csoport</span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;34608877" target="_blank">Efficient Annotation Methods for Industrial Datasets using Artificial Intelligence</a></div> <div class="InBook"><div class="chapter-in">In:</div> <div class="authors"> <div class="autype autype-1"> <span class="author-name" mtid="10001498"><a href="/gui2/?type=authors&mode=browse&sel=10001498" target="_blank">Vajk István (<span class="authorship-author-name">Vajk István</span> <span class="authorAux-mtmt"> Irányítástechnika, műszaki informatika</span>) </a> </span> <span class="author-affil"><span title="Budapesti Műszaki és Gazdaságtudományi Egyetem">BME</span>/<span title="Villamosmérnöki és Informatikai Kar">VIK</span>/Automatizálási és Alkalmazott Informatikai Tanszék</span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" mtid="10042251"><a href="/gui2/?type=authors&mode=browse&sel=10042251" target="_blank">Dmitriy Dunaev (<span class="authorship-author-name">Dunaev Dmitriy</span> <span class="authorAux-mtmt"> Computer Science</span>) </a> </span> <span class="author-affil"><span title="Budapesti Műszaki és Gazdaságtudományi Egyetem">BME</span>/<span title="Villamosmérnöki és Informatikai Kar">VIK</span>/Automatizálási és Alkalmazott Informatikai Tanszék</span> (szerk.) </div> </div> <div class="booktitle"><a href="/gui2/?mode=browse&params=publication;34051102" target="_blank">Proceedings of the Automation and Applied Computer Science Workshop 2023 (AACS'23) </a></div> <div class="conference"> Konferencia helye, ideje: <span class="location">Budapest, Magyarország <span class="conference-date">2023.07.07. - 2023.07.07.</span> (<span class="conference-organizer">Budapest University of Technology and Economics, Department of Automation and Applied Informatics) </div> <span class="publishedAt">Budapest: <span class="publishers">Budapesti Műszaki Egyetem, Automatizálási és Alkalmazott Informatikai Tanszék</span>, <span class="page"> pp 64-75 </span> <span class="year">(2023)</span> </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> </span> <span class="bookchapter-ids">Befoglaló link(ek):</span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <span class="isbnOrIssn"> ISBN: </span> <a style="color:black" title="9789634219262" target="_blank" href="https://www.worldcat.org/search?q=isbn%3A9789634219262"> 9789634219262 </a> </span> </span> <div class="mtid"><span class="long-pub-mtid">Közlemény: 34608877</span> | <span class="status-data status-APPROVED"> Nyilvános </span> <span class="long-book-mtid">Befoglaló: 34051102</span> Forrás | <span class="type-subtype">Könyvrészlet ( Konferenciaközlemény ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">kézi felvitel</span> </div> <div class="lastModified">Utolsó módosítás: 2024.02.29. 08:28 Szécsényi Nándor (mesterséges intelligencia alkalmazása teljesítményelektronikában) </div> </div> </div>