Laser & Optoelectronics Progress, Volume. 58, Issue 22, 2211004(2021)

Convolutional Neural Network Image Feature Measurement Based on Information Entropy

Wenjun Chen, Chao Cong*, and Liwen Huang
Author Affiliations
  • College of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China
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    Figures & Tables(10)
    Examples of different activations of feature layers. (a) Example of fully activated neurons; (b) example of insufficient activation of neurons; (c) example of full activation of deep layer neurons
    Flow chart of image feature measurement algorithm based on information entropy
    Feature purity of each feature layer of ResNet18 and VGG19 under different thresholds. (a) ResNet18; (b) VGG19
    Comparison of Grad-CAM and feature purity of ResNet18 feature layer of different types of images in CIFAR10 dataset
    Comparison of feature activation maps and feature purity of feature layers on ImageNet1000 for models with different performance
    Comparison of Grad-CAM and feature purity of last feature layer under different epochs of Tiny VGG
    Comparison of Grad-CAM and feature purity of last feature layer of ResNet18 model under different epochs
    • Table 1. Comparison of feature purity of different feature layers of VGG and ResNet models

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      Table 1. Comparison of feature purity of different feature layers of VGG and ResNet models

      LayerVGG 13_bnVGG 16_bnVGG 19_bnResNet34ResNet50ResNet101
      C10.4070.5590.5500.6290.6920.644
      C20.4040.3080.1350.5060.6410.739
      C30.4090.2830.4070.2260.4910.575
      C40.7630.8200.7770.4430.3370.515
      C50.9300.9340.9390.9310.9480.952
      Average0.5830.5810.5620.5470.6220.639
    • Table 2. Comparison of cross-model feature purity on ImageNet1000 dataset

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      Table 2. Comparison of cross-model feature purity on ImageNet1000 dataset

      DatasetAlexNetVGG16DenseNet121ResNet50SENet154
      Accuracy /%56.4371.6474.6776.0081.30
      Purity0.5120.7930.8700.9480.963
    • Table 3. Comparison of feature purity scores of ResNet18 model and Tiny VGG model under different training epochs

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      Table 3. Comparison of feature purity scores of ResNet18 model and Tiny VGG model under different training epochs

      ModelAccuracyPurityModelAccuracyPurity
      Tiny VGG_1081.250.713ResNet18_1087.500.810
      Tiny VGG_2081.250.756ResNet18_2087.500.826
      Tiny VGG_5085.500.823ResNet18_5093.750.887
      Tiny VGG_10087.500.835ResNet18_10095.500.946
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    Wenjun Chen, Chao Cong, Liwen Huang. Convolutional Neural Network Image Feature Measurement Based on Information Entropy[J]. Laser & Optoelectronics Progress, 2021, 58(22): 2211004

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    Paper Information

    Category: Imaging Systems

    Received: Dec. 16, 2020

    Accepted: Feb. 12, 2021

    Published Online: Nov. 5, 2021

    The Author Email: Chao Cong (chenwj@2019.cqut.edu.cn)

    DOI:10.3788/LOP202158.2211004

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