An Adaptive Batch Normalization in Deep Learning

• URL: http://arxiv.org/abs/2211.02050v1
• Date: Thu, 3 Nov 2022 12:12:56 GMT
• Title: An Adaptive Batch Normalization in Deep Learning
• Authors: Wael Alsobhi, Tarik Alafif, Alaa Abdel-Hakim, Weiwei Zong
• Abstract summary: Batch Normalization (BN) is a way to accelerate and stabilize training in deep convolutional neural networks. We propose a threshold-based adaptive BN approach that separates the data that requires the BN and data that does not require it.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Batch Normalization (BN) is a way to accelerate and stabilize training in deep convolutional neural networks. However, the BN works continuously within the network structure, although some training data may not always require it. In this research work, we propose a threshold-based adaptive BN approach that separates the data that requires the BN and data that does not require it. The experimental evaluation demonstrates that proposed approach achieves better performance mostly in small batch sizes than the traditional BN using MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100. It also reduces the occurrence of internal variable transformation to increase network stability

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