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dc.contributor.authorLi, Zewen
dc.contributor.authorLiu, Fan
dc.contributor.authorYang, Wenjie
dc.contributor.authorPeng, Shouheng
dc.contributor.authorZhou, Jun
dc.date.accessioned2021-06-16T01:09:28Z
dc.date.available2021-06-16T01:09:28Z
dc.date.issued2021
dc.identifier.issn2162-237X
dc.identifier.doi10.1109/tnnls.2021.3084827
dc.identifier.urihttp://hdl.handle.net/10072/405164
dc.description.abstractA convolutional neural network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention from both industry and academia in the past few years. The existing reviews mainly focus on CNN's applications in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered. In this review, we aim to provide some novel ideas and prospects in this fast-growing field. Besides, not only 2-D convolution but also 1-D and multidimensional ones are involved. First, this review introduces the history of CNN. Second, we provide an overview of various convolutions. Third, some classic and advanced CNN models are introduced; especially those key points making them reach state-of-the-art results. Fourth, through experimental analysis, we draw some conclusions and provide several rules of thumb for functions and hyperparameter selection. Fifth, the applications of 1-D, 2-D, and multidimensional convolution are covered. Finally, some open issues and promising directions for CNN are discussed as guidelines for future work.
dc.description.peerreviewedYes
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofjournalIEEE Transactions on Neural Networks and Learning Systems
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.titleA Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationLi, Z; Liu, F; Yang, W; Peng, S; Zhou, J, A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects, IEEE Transactions on Neural Networks and Learning Systems, 2021
dc.date.updated2021-06-16T01:04:05Z
dc.description.versionAccepted Manuscript (AM)
gro.description.notepublicThis publication has been entered in Griffith Research Online as an advanced online version.
gro.rights.copyright© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun


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