CL-SR: Boosting Imbalanced Image Classification with Contrastive Learning and Synthetic Minority Oversampling Technique Based on Rough Set Theory Integration

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CL-SR: Boosting Imbalanced Image Classification with Contrastive Learning and Synthetic Minority Oversampling Technique Based on Rough Set Theory Integration

Image recognition models often struggle with class imbalance, which can impede their performance. To overcome this issue, researchers have extensively used resampling methods, traditionally focused on tabular datasets. In contrast to the original method, which generates data at the data level, this...

書目詳細資料
發表在:	Applied Sciences (Switzerland)
主要作者:	Gao X.; Jamil N.; Ramli M.I.
格式:	Article
語言:	English
出版:	Multidisciplinary Digital Publishing Institute (MDPI) 2024
在線閱讀:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212100370&doi=10.3390%2fapp142311093&partnerID=40&md5=9d4bce580aad2b95262de5a0de28d5a2

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