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...
| الحاوية / القاعدة: | Applied Sciences (Switzerland) |
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| المؤلف الرئيسي: | |
| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
Multidisciplinary Digital Publishing Institute (MDPI)
2024
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| الوصول للمادة أونلاين: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212100370&doi=10.3390%2fapp142311093&partnerID=40&md5=9d4bce580aad2b95262de5a0de28d5a2 |