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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...

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Hlavní autor: Mehdi Ghayoumi
Médium: Artigo
Jazyk:Inglês
Vydáno: MDPI AG 2025-05-01
Edice:AI
Témata:
convolutional neural networks (CNNs)
probabilistic feature importance dropout (PFID)
regularization techniques
adaptive learning
network efficiency
On-line přístup:https://www.mdpi.com/2673-2688/6/6/111
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