Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (6): 1905-1910.DOI: 10.11772/j.issn.1001-9081.2023050656
Special Issue: 先进计算
• Advanced computing • Previous Articles Next Articles
Received:
2023-05-26
Revised:
2023-08-03
Accepted:
2023-08-08
Online:
2023-08-10
Published:
2024-06-10
Contact:
Handa MA
About author:
ZHAI Feiyu, born in 1999, M. S. candidate. His research interests include image classification, quantum computing.
Supported by:
通讯作者:
马汉达
作者简介:
翟飞宇(1999—),男,江苏南通人,硕士研究生,主要研究方向:图像分类、量子计算;
基金资助:
CLC Number:
Feiyu ZHAI, Handa MA. Hybrid classical-quantum classification model based on DenseNet[J]. Journal of Computer Applications, 2024, 44(6): 1905-1910.
翟飞宇, 马汉达. 基于DenseNet的经典-量子混合分类模型[J]. 《计算机应用》唯一官方网站, 2024, 44(6): 1905-1910.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023050656
模型 | Chinese Medicine | CIFAR-100 | ||||||
---|---|---|---|---|---|---|---|---|
acc | pre | rec | acc | pre | rec | |||
AlexNet | 90.6 | 90.8 | 90.8 | 90.5 | 47.5 | 48.2 | 47.5 | 47.4 |
GoogLeNet | 76.2 | 77.2 | 76.0 | 76.3 | 57.5 | 57.3 | 58.4 | 56.1 |
VGG19 | 80.7 | 80.4 | 80.4 | 80.2 | 56.8 | 57.2 | 56.8 | 56.7 |
ResNet | 76.8 | 77.5 | 76.7 | 76.8 | 57.8 | 58.5 | 57.8 | 57.8 |
DenseNet‑169 | 86.7 | 87.0 | 84.7 | 84.5 | 58.1 | 58.5 | 58.1 | 58.4 |
CQDenseNet | 92.8 | 93.0 | 93.0 | 92.8 | 65.5 | 65.8 | 65.5 | 64.8 |
Tab. 1 Performance comparison among various models
模型 | Chinese Medicine | CIFAR-100 | ||||||
---|---|---|---|---|---|---|---|---|
acc | pre | rec | acc | pre | rec | |||
AlexNet | 90.6 | 90.8 | 90.8 | 90.5 | 47.5 | 48.2 | 47.5 | 47.4 |
GoogLeNet | 76.2 | 77.2 | 76.0 | 76.3 | 57.5 | 57.3 | 58.4 | 56.1 |
VGG19 | 80.7 | 80.4 | 80.4 | 80.2 | 56.8 | 57.2 | 56.8 | 56.7 |
ResNet | 76.8 | 77.5 | 76.7 | 76.8 | 57.8 | 58.5 | 57.8 | 57.8 |
DenseNet‑169 | 86.7 | 87.0 | 84.7 | 84.5 | 58.1 | 58.5 | 58.1 | 58.4 |
CQDenseNet | 92.8 | 93.0 | 93.0 | 92.8 | 65.5 | 65.8 | 65.5 | 64.8 |
模型 | 方法 | 准确率 |
---|---|---|
文献[ | CGAN+QML,CML | 96.00 |
文献[ | QNN | 96.92 |
本文模型 | DenseNet+VQC+迁移学习 | 98.00 |
Tab.2 Performance comparison between proposed model and models based on quantum computing module
模型 | 方法 | 准确率 |
---|---|---|
文献[ | CGAN+QML,CML | 96.00 |
文献[ | QNN | 96.92 |
本文模型 | DenseNet+VQC+迁移学习 | 98.00 |
预处理 | Chinese Medicine | CIFAR-100 | ||||||
---|---|---|---|---|---|---|---|---|
acc | pre | rec | acc | pre | rec | |||
均值模糊 | 92.0 | 92.5 | 92.0 | 92.0 | 64.5 | 64.8 | 65.0 | 64.6 |
颜色空间变换 | 92.7 | 92.5 | 92.2 | 92.4 | 65.1 | 65.5 | 65.0 | 64.5 |
无 | 92.8 | 93.0 | 93.0 | 92.8 | 65.5 | 65.8 | 65.5 | 64.8 |
Tab.3 Performance effect comparison on CQDenseNet by two different preprocessing methods
预处理 | Chinese Medicine | CIFAR-100 | ||||||
---|---|---|---|---|---|---|---|---|
acc | pre | rec | acc | pre | rec | |||
均值模糊 | 92.0 | 92.5 | 92.0 | 92.0 | 64.5 | 64.8 | 65.0 | 64.6 |
颜色空间变换 | 92.7 | 92.5 | 92.2 | 92.4 | 65.1 | 65.5 | 65.0 | 64.5 |
无 | 92.8 | 93.0 | 93.0 | 92.8 | 65.5 | 65.8 | 65.5 | 64.8 |
模型 | Chinese Medicine | CIFAR-100 | ||||||
---|---|---|---|---|---|---|---|---|
acc | pre | rec | acc | pre | rec | |||
QDenseNet | 91.7 | 91.9 | 91.7 | 91.7 | 63.9 | 64.3 | 73.8 | 63.0 |
CQDenseNet | 92.8 | 93.0 | 93.0 | 92.8 | 65.5 | 65.8 | 65.5 | 64.8 |
Tab. 4 Influence of transfer learning on hybrid model performance
模型 | Chinese Medicine | CIFAR-100 | ||||||
---|---|---|---|---|---|---|---|---|
acc | pre | rec | acc | pre | rec | |||
QDenseNet | 91.7 | 91.9 | 91.7 | 91.7 | 63.9 | 64.3 | 73.8 | 63.0 |
CQDenseNet | 92.8 | 93.0 | 93.0 | 92.8 | 65.5 | 65.8 | 65.5 | 64.8 |
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