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. This study ai...
Kaydedildi:
| Yazar: | |
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| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
MDPI AG
2025-05-01
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| Seri Bilgileri: | AI |
| Konular: | |
| Online Erişim: | https://www.mdpi.com/2673-2688/6/6/111 |
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