CRSSC: Salvage Reusable Samples from Noisy Data for Robust Learning

Sun, Z Hua, X-S Yao, Y Wei, X-S Hu, G Zhang, J

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 28th ACM International Conference on Multimedia, 2020, pp. 92-101
Issue Date:: 2020-10-12

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Field	Value	Language
dc.contributor.author	Sun, Z
dc.contributor.author	Hua, X-S
dc.contributor.author	Yao, Y
dc.contributor.author	Wei, X-S
dc.contributor.author	Hu, G
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541
dc.date.accessioned	2021-04-11T06:40:25Z
dc.date.available	2021-04-11T06:40:25Z
dc.date.issued	2020-10-12
dc.identifier.citation	Proceedings of the 28th ACM International Conference on Multimedia, 2020, pp. 92-101
dc.identifier.isbn	9781450379885
dc.identifier.uri	http://hdl.handle.net/10453/147989
dc.language	en
dc.publisher	ACM
dc.relation.ispartof	Proceedings of the 28th ACM International Conference on Multimedia
dc.relation.ispartof	MM '20: The 28th ACM International Conference on Multimedia
dc.relation.isbasedon	10.1145/3394171.3413978
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	CRSSC: Salvage Reusable Samples from Noisy Data for Robust Learning
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2021-04-11T06:39:40Z
pubs.publication-status	Published

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