On the Robustness of Compressed Models with Class Imbalance
Deep learning (DL) models have been deployed in various platforms, including resource-constrained environments such as edge computing, smartphones, and personal devices. Such deployment requires models to have smaller sizes and memory footprints. To this end, many model compression techniques propos...
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MDPI AG
2024-11-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/13/11/297 |
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| author | Baraa Saeed Ali Nabil Sarhan Mohammed Alawad |
| author_facet | Baraa Saeed Ali Nabil Sarhan Mohammed Alawad |
| author_sort | Baraa Saeed Ali |
| collection | DOAJ |
| description | Deep learning (DL) models have been deployed in various platforms, including resource-constrained environments such as edge computing, smartphones, and personal devices. Such deployment requires models to have smaller sizes and memory footprints. To this end, many model compression techniques proposed in the literature successfully reduce model sizes and maintain comparable accuracy. However, the robustness of compressed DL models against class imbalance, a natural phenomenon in real-life datasets, is still under-explored. We present a comprehensive experimental study of the performance and robustness of compressed DL models when trained on class-imbalanced datasets. We investigate the robustness of compressed DL models using three popular compression techniques (pruning, quantization, and knowledge distillation) with class-imbalanced variants of the CIFAR-10 dataset and show that compressed DL models are not robust against class imbalance in training datasets. We also show that different compression techniques have varying degrees of impact on the robustness of compressed DL models. |
| format | Article |
| id | doaj-art-43fa7f07f0fd4f339f757e8281bf09ce |
| institution | OA Journals |
| issn | 2073-431X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-43fa7f07f0fd4f339f757e8281bf09ce2025-08-20T02:08:14ZengMDPI AGComputers2073-431X2024-11-01131129710.3390/computers13110297On the Robustness of Compressed Models with Class ImbalanceBaraa Saeed Ali0Nabil Sarhan1Mohammed Alawad2Electrical Engineering Department, University of Anbar, Ramadi, Anbar 55431, IraqDepartment of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USADepartment of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USADeep learning (DL) models have been deployed in various platforms, including resource-constrained environments such as edge computing, smartphones, and personal devices. Such deployment requires models to have smaller sizes and memory footprints. To this end, many model compression techniques proposed in the literature successfully reduce model sizes and maintain comparable accuracy. However, the robustness of compressed DL models against class imbalance, a natural phenomenon in real-life datasets, is still under-explored. We present a comprehensive experimental study of the performance and robustness of compressed DL models when trained on class-imbalanced datasets. We investigate the robustness of compressed DL models using three popular compression techniques (pruning, quantization, and knowledge distillation) with class-imbalanced variants of the CIFAR-10 dataset and show that compressed DL models are not robust against class imbalance in training datasets. We also show that different compression techniques have varying degrees of impact on the robustness of compressed DL models.https://www.mdpi.com/2073-431X/13/11/297class imbalancedeep learningmodel compressionrobustness |
| spellingShingle | Baraa Saeed Ali Nabil Sarhan Mohammed Alawad On the Robustness of Compressed Models with Class Imbalance Computers class imbalance deep learning model compression robustness |
| title | On the Robustness of Compressed Models with Class Imbalance |
| title_full | On the Robustness of Compressed Models with Class Imbalance |
| title_fullStr | On the Robustness of Compressed Models with Class Imbalance |
| title_full_unstemmed | On the Robustness of Compressed Models with Class Imbalance |
| title_short | On the Robustness of Compressed Models with Class Imbalance |
| title_sort | on the robustness of compressed models with class imbalance |
| topic | class imbalance deep learning model compression robustness |
| url | https://www.mdpi.com/2073-431X/13/11/297 |
| work_keys_str_mv | AT baraasaeedali ontherobustnessofcompressedmodelswithclassimbalance AT nabilsarhan ontherobustnessofcompressedmodelswithclassimbalance AT mohammedalawad ontherobustnessofcompressedmodelswithclassimbalance |