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...

書誌詳細
出版年:	Applied Sciences (Switzerland)
第一著者:	Gao X.; Jamil N.; Ramli M.I.
フォーマット:	論文
言語:	English
出版事項:	Multidisciplinary Digital Publishing Institute (MDPI) 2024
オンライン･アクセス:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212100370&doi=10.3390%2fapp142311093&partnerID=40&md5=9d4bce580aad2b95262de5a0de28d5a2

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