Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (2): 395-403.DOI: 10.11772/j.issn.1001-9081.2021020367
• Artificial intelligence • Previous Articles
Xinyu CHEN1, Mingzhe LIU1(), Jun REN2, Ying TANG3
Received:
2021-03-11
Revised:
2021-07-16
Accepted:
2021-07-20
Online:
2021-08-04
Published:
2022-02-10
Contact:
Mingzhe LIU
About author:
CHEN Xinyu, born in 1996, M. S. candidate. Her research interests include deep learning, computer vision.Supported by:
通讯作者:
刘明哲
作者简介:
陈薪羽(1996—),女,重庆人,硕士研究生,主要研究方向:深度学习、计算机视觉;基金资助:
CLC Number:
Xinyu CHEN, Mingzhe LIU, Jun REN, Ying TANG. Parameter asynchronous updating algorithm based on multi-column convolutional neural network[J]. Journal of Computer Applications, 2022, 42(2): 395-403.
陈薪羽, 刘明哲, 任俊, 汤影. 基于多列卷积神经网络的参数异步更新算法[J]. 《计算机应用》唯一官方网站, 2022, 42(2): 395-403.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021020367
模型 | MAE | MSE |
---|---|---|
CCNN(Crowd CNN)[ | 465.90 | 499.80 |
C-MTL(Cascaded Multi-Task Learning) [ | 411.80 | 561.90 |
IG-CNN(Incrementally Growing CNN) [ | 323.70 | 399.20 |
CSRNet(Congested Scenes CNN) [ | 268.90 | 398.40 |
SwitchCNN[ | 321.60 | 443.40 |
CP-CNN[ | 296.90 | 324.80 |
ic-CNN[ | 261.80 | 366.40 |
ACSCP(Adversarial Cross-Scale Consistency Pursuit) [ | 291.70 | 405.10 |
Deep-NCL(Negative Correlation Learning) [ | 289.10 | 405.20 |
MCNN[ | 378.90 | 510.40 |
SaCNN(Scale-adaptive CNN) [ | 316.20 | 426.10 |
ic-CNN+McML[ | 244.80 | 359.20 |
A-MCNN | 242.10 | 310.80 |
Tab. 1 Comparison of experimental results on UCSD dataset
模型 | MAE | MSE |
---|---|---|
CCNN(Crowd CNN)[ | 465.90 | 499.80 |
C-MTL(Cascaded Multi-Task Learning) [ | 411.80 | 561.90 |
IG-CNN(Incrementally Growing CNN) [ | 323.70 | 399.20 |
CSRNet(Congested Scenes CNN) [ | 268.90 | 398.40 |
SwitchCNN[ | 321.60 | 443.40 |
CP-CNN[ | 296.90 | 324.80 |
ic-CNN[ | 261.80 | 366.40 |
ACSCP(Adversarial Cross-Scale Consistency Pursuit) [ | 291.70 | 405.10 |
Deep-NCL(Negative Correlation Learning) [ | 289.10 | 405.20 |
MCNN[ | 378.90 | 510.40 |
SaCNN(Scale-adaptive CNN) [ | 316.20 | 426.10 |
ic-CNN+McML[ | 244.80 | 359.20 |
A-MCNN | 242.10 | 310.80 |
模型 | ShanghaiTech Part_A | ShanghaiTech Part_B | ||
---|---|---|---|---|
MAE | MSE | MAE | MSE | |
CCNN[ | 185.30 | 280.50 | 34.20 | 51.30 |
C-MTL[ | 103.90 | 155.70 | 20.90 | 32.60 |
IG-CNN[ | 73.40 | 119.50 | 14.30 | 22.90 |
CSRNet[ | 69.30 | 116.10 | 10.80 | 17.10 |
SwitchCNN[ | 92.60 | 133.70 | 23.80 | 31.50 |
CP-CNN[ | 74.80 | 107.90 | 21.20 | 31.80 |
ic-CNN[ | 70.20 | 117.30 | 11.10 | 17.00 |
ACSCP[ | 76.20 | 103.50 | 17.90 | 28.40 |
Deep-NCL[ | 74.10 | 113.50 | 19.20 | 26.90 |
MCNN[ | 111.50 | 175.60 | 27.10 | 42.20 |
SaCNN[ | 87.20 | 140.70 | 16.90 | 26.30 |
ic-CNN+McML[ | 64.20 | 112.30 | 10.40 | 14.20 |
A-MCNN | 63.10 | 100.20 | 8.50 | 9.20 |
Tab. 2 Comparison of experimental results on ShanghaiTech datasets
模型 | ShanghaiTech Part_A | ShanghaiTech Part_B | ||
---|---|---|---|---|
MAE | MSE | MAE | MSE | |
CCNN[ | 185.30 | 280.50 | 34.20 | 51.30 |
C-MTL[ | 103.90 | 155.70 | 20.90 | 32.60 |
IG-CNN[ | 73.40 | 119.50 | 14.30 | 22.90 |
CSRNet[ | 69.30 | 116.10 | 10.80 | 17.10 |
SwitchCNN[ | 92.60 | 133.70 | 23.80 | 31.50 |
CP-CNN[ | 74.80 | 107.90 | 21.20 | 31.80 |
ic-CNN[ | 70.20 | 117.30 | 11.10 | 17.00 |
ACSCP[ | 76.20 | 103.50 | 17.90 | 28.40 |
Deep-NCL[ | 74.10 | 113.50 | 19.20 | 26.90 |
MCNN[ | 111.50 | 175.60 | 27.10 | 42.20 |
SaCNN[ | 87.20 | 140.70 | 16.90 | 26.30 |
ic-CNN+McML[ | 64.20 | 112.30 | 10.40 | 14.20 |
A-MCNN | 63.10 | 100.20 | 8.50 | 9.20 |
模型 | MAE | MSE |
---|---|---|
CCNN[ | 1.52 | 3.21 |
BSA-CNN[ | 1.06 | 1.49 |
CSRNet[ | 1.18 | 1.55 |
SwitchCNN[ | 1.69 | 2.48 |
MCNN+McML[ | 1.06 | 1.29 |
ic-CNN[ | 1.17 | 1.49 |
CSRNet+McML[ | 1.05 | 1.31 |
MCNN[ | 1.09 | 1.39 |
ic-CNN+McML[ | 1.05 | 1.23 |
A-MCNN | 1.03 | 1.11 |
Tab. 3 Comparison of experimental results on UCF_CC_50 dataset
模型 | MAE | MSE |
---|---|---|
CCNN[ | 1.52 | 3.21 |
BSA-CNN[ | 1.06 | 1.49 |
CSRNet[ | 1.18 | 1.55 |
SwitchCNN[ | 1.69 | 2.48 |
MCNN+McML[ | 1.06 | 1.29 |
ic-CNN[ | 1.17 | 1.49 |
CSRNet+McML[ | 1.05 | 1.31 |
MCNN[ | 1.09 | 1.39 |
ic-CNN+McML[ | 1.05 | 1.23 |
A-MCNN | 1.03 | 1.11 |
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