Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout
<b>Background/Objectives:</b> Convolutional Neural Networks (CNNs), while effective in tasks such as image classification and language processing, often experience overfitting and inefficient training due to static, structure-agnostic regularization techniques like traditional dropout. T...
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MDPI AG
2025-05-01
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| author | Mehdi Ghayoumi |
| author_facet | Mehdi Ghayoumi |
| author_sort | Mehdi Ghayoumi |
| collection | DOAJ |
| description | <b>Background/Objectives:</b> Convolutional Neural Networks (CNNs), while effective in tasks such as image classification and language processing, often experience overfitting and inefficient training due to static, structure-agnostic regularization techniques like traditional dropout. This study aims to address these limitations by proposing a more dynamic and context-sensitive dropout strategy. <b>Methods:</b> We introduce <i>Probabilistic Feature Importance Dropout</i> (PFID), a novel regularization method that assigns dropout rates based on the probabilistic significance of individual features. PFID is integrated with adaptive, structured, and contextual dropout strategies, forming a unified framework for intelligent regularization. <b>Results:</b> Experimental evaluation on standard benchmark datasets including CIFAR-10, MNIST, and Fashion MNIST demonstrated that PFID significantly improves performance metrics such as classification accuracy, training loss, and computational efficiency compared to conventional dropout methods. <b>Conclusions:</b> PFID offers a practical and scalable solution for enhancing CNN generalization and training efficiency. Its dynamic nature and feature-aware design provide a strong foundation for future advancements in adaptive regularization for deep learning models. |
| format | Article |
| id | doaj-art-c460fe7589c645ab95c8b691b95ea4c8 |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-c460fe7589c645ab95c8b691b95ea4c82025-08-20T03:24:29ZengMDPI AGAI2673-26882025-05-016611110.3390/ai6060111Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized DropoutMehdi Ghayoumi0Department of Cybersecurity, School of Science, Health and Criminal Justice, State University of New York, Canton, NY 13617, USA<b>Background/Objectives:</b> Convolutional Neural Networks (CNNs), while effective in tasks such as image classification and language processing, often experience overfitting and inefficient training due to static, structure-agnostic regularization techniques like traditional dropout. This study aims to address these limitations by proposing a more dynamic and context-sensitive dropout strategy. <b>Methods:</b> We introduce <i>Probabilistic Feature Importance Dropout</i> (PFID), a novel regularization method that assigns dropout rates based on the probabilistic significance of individual features. PFID is integrated with adaptive, structured, and contextual dropout strategies, forming a unified framework for intelligent regularization. <b>Results:</b> Experimental evaluation on standard benchmark datasets including CIFAR-10, MNIST, and Fashion MNIST demonstrated that PFID significantly improves performance metrics such as classification accuracy, training loss, and computational efficiency compared to conventional dropout methods. <b>Conclusions:</b> PFID offers a practical and scalable solution for enhancing CNN generalization and training efficiency. Its dynamic nature and feature-aware design provide a strong foundation for future advancements in adaptive regularization for deep learning models.https://www.mdpi.com/2673-2688/6/6/111convolutional neural networks (CNNs)probabilistic feature importance dropout (PFID)regularization techniquesadaptive learningnetwork efficiency |
| spellingShingle | Mehdi Ghayoumi Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout AI convolutional neural networks (CNNs) probabilistic feature importance dropout (PFID) regularization techniques adaptive learning network efficiency |
| title | Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout |
| title_full | Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout |
| title_fullStr | Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout |
| title_full_unstemmed | Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout |
| title_short | Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout |
| title_sort | enhancing efficiency and regularization in convolutional neural networks strategies for optimized dropout |
| topic | convolutional neural networks (CNNs) probabilistic feature importance dropout (PFID) regularization techniques adaptive learning network efficiency |
| url | https://www.mdpi.com/2673-2688/6/6/111 |
| work_keys_str_mv | AT mehdighayoumi enhancingefficiencyandregularizationinconvolutionalneuralnetworksstrategiesforoptimizeddropout |