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Channel modulus normalization for CNN image classification
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DOI：10.1007/s00530-024-01468-9.png)
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Abstract
En 中文
This paper introduces a novel normalization operation, Channel Modulus Normalization (CMN), to enhance the image classification performance of traditional convolutional neural networks (CNNs) by leveraging latent feature characteristics across all levels. CMN is seamlessly integrated into existing CNN architectures without using additional trainable parameters. Following convolution operations at every level, CMN is applied to each feature map, utilizing a normalization adjustment factor to mitigate the influence of feature modulus length. This operation pays more attention to the change characteristics of the image details for classification. Experiments on six datasets such as CIFAR10, CIFAR100, FaceScrub, Tiny ImageNet, ImageNet (100), and ImageNet (1000), show that the channel modulus normalization operation can effectively improve the classification accuracy of the datasets above. Taking ResNet50 as an example, it is increased by 0.57%, 3.17%, 1.11%, 2.28%, 0.61% and 0.14% respectively. At the same time, the normalization operation will not increase the number of parameters of the model, and the convergence performance during training is better, which will only increase a small amount of calculation. The implementation code is available at https://github.com/99-WSJ/Channel-Modulus-Normalization.
Keywords:
Channel modulus
Normalization
Feature extraction
Convolutional neural network
Image classification
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2.8K
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