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	2021-01-25T19:32:44Z
dc.date.available	2021-01-25T19:32:44Z
dc.date.issued	2019
dc.identifier	10.1109/LAWP.2019.2930602
dc.identifier.issn	1536-1225
dc.identifier.uri	https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/7373
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-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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.woscategory	Engineering, Electrical & Electronic; Telecommunications
dc.description.wosresearcharea	Engineering; Telecommunications
dc.identifier.wosid	WOS:000498566200011

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