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...
| 出版年: | Applied Sciences (Switzerland) |
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| 第一著者: | |
| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Multidisciplinary Digital Publishing Institute (MDPI)
2024
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| 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 |