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dc.contributor.authorIslam, SM Sofiqul
dc.contributor.authorRahman, Shanto
dc.contributor.authorRahman, Md Mostafijur
dc.contributor.authorDey, Emon Kumar
dc.contributor.authorShoyaib, Mohammad
dc.date.accessioned2021-05-14T04:28:51Z
dc.date.available2021-05-14T04:28:51Z
dc.date.issued2016
dc.identifier.doi10.1109/iciev.2016.7760071
dc.identifier.urihttp://hdl.handle.net/10072/404401
dc.description.abstractDeep learning is a new era of machine learning research, where many layers of information processing stages are exploited for unsupervised feature learning. Using multiple levels of representation and abstraction, it helps a machine to understand about data (e.g., images, sound and text) more accurately. Many deep learning models have been proposed for solving the problem of different applications. Therefore, a comprehensive knowledge of these models is demanded to select the appropriate one for a specific application areas in signal or data processing. This paper reviews several deep learning models proposed for different application area in the field of computer vision, and makes a comprehensive evaluation of two well-known models namely AlexNet and VGG_S in nine different benchmark datasets. The experimental results show that these two models perform better than the existing state-of-the-art deep learning models in one dataset.
dc.publisherIEEE
dc.relation.ispartofconferencename2016 International Conference on Informatics, Electronics and Vision (ICIEV)
dc.relation.ispartofconferencetitle2016 5th International Conference on Informatics, Electronics and Vision (ICIEV)
dc.relation.ispartofdatefrom2016-05-13
dc.relation.ispartofdateto2016-05-14
dc.titleApplication of deep learning to computer vision: A comprehensive study
dc.typeConference output
dcterms.bibliographicCitationIslam, SMS; Rahman, S; Rahman, MM; Dey, EK; Shoyaib, M, Application of deep learning to computer vision: A comprehensive study, 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), 2016
dc.date.updated2021-05-14T04:27:56Z
gro.hasfulltextNo Full Text
gro.griffith.authorDey, Emon Kumar


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