Transferring Synthetic Elementary Learning Tasks to Classification of Complex Targets

dc.contributor.author	Selver, M.A.
dc.contributor.author	Toprak, T.
dc.contributor.author	Secmen, M.
dc.contributor.author	Zoral, E.Y.
dc.date.accessioned	2021-01-25T20:48:09Z
dc.date.available	2021-01-25T20:48:09Z
dc.date.issued	2019
dc.identifier	10.1109/LAWP.2019.2930602
dc.identifier.issn	15361225
dc.identifier.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074992521&doi=10.1109%2fLAWP.2019.2930602&partnerID=40&md5=38f7ffdaad86d25e6551e357baa8f347
dc.identifier.uri	https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/9770
dc.description.abstract	Deep learning has a promising impact on target classification performance at the expense of huge training data requirements. Therefore, the use of simulated data is inevitable for convergence of deep models (DMs). However, generating synthetic data for re
dc.language.iso	English
dc.publisher	IEEE Antennas and Wireless Propagation Letters
dc.title	Transferring Synthetic Elementary Learning Tasks to Classification of Complex Targets
dc.type	Article
dc.relation.firstpage	2267
dc.relation.lastpage	2271
dc.relation.volume	18
dc.relation.issue	11
dc.description.affiliations	Department of Electrical and Electronics Engineering, Dokuz Eylül University, Buca, 35220, Turkey; Department of Electrical and Electronics Engineering, Yasar University, Bornova, 35100, Turkey

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