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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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/6/6/111 |
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