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Algorithm of overfitting avoidance in CNN based on maximum pooled and weight decay

Guanzhan Li

Guanzhan Li

Jiangxi University of EngineeringXinyu, China
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Li, Guanzhan
, 
Xiangcheng Jian

Xiangcheng Jian

Jiangxi University of EngineeringXinyu, China
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Jian, Xiangcheng
, 
Zhicheng Wen

Zhicheng Wen

Jiangxi University of EngineeringXinyu, China
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Wen, Zhicheng
 et 
Jamal AlSultan

Jamal AlSultan

Computer Science, College of Arts & Science, Applied Science University-BahrainBahrain
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AlSultan, Jamal
  
22 août 2022
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Publié en ligne: 22 août 2022
Pages: 965 - 974
Reçu: 25 oct. 2021
Accepté: 15 mai 2022
DOI: https://doi.org/10.2478/amns.2022.1.00011
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© 2022 Guanzhan Li et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Operation diagram of the mask based on maximum pooled.
Operation diagram of the mask based on maximum pooled.

Fig. 2

Some samples of CVC-ClincDB and CIFAR-10 data sets.
Some samples of CVC-ClincDB and CIFAR-10 data sets.

Fig. 3

Curve of iteration times, loss value and accuracy in CVC-ClincDB dataset.
Curve of iteration times, loss value and accuracy in CVC-ClincDB dataset.

Fig. 4

Curve of iteration times, loss value and accuracy in the CIFAR-10 dataset.
Curve of iteration times, loss value and accuracy in the CIFAR-10 dataset.

The error rate (%) under different maintenance probabilities in CVC-ClincDB_

p 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
ICNN 6.36 6.36 6.36 6.36 6.36 6.36 6.36 6.36 6.36
INN_FD 7.27 6.85 6.50 6.23 6.20 6.31 6.38 6.40 6.51
SCP 2.23 2.17 2.10 2.12 2.08 2.26 2.31 2.37 2.49
MD_WD 2.21 2.15 2.03 1.95 1.63 1.88 2.19 2.26 2.27

The error rate (%) under different maintenance probabilities in CIFAR-10_

p 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
ICNN 7.44 7.44 7.44 7.44 7.44 7.44 7.44 7.44 7.44
INN_FD 8.87 7.95 7.76 7.10 6.15 7.21 7.38 8.12 8.69
SCP 4.83 4.67 4.55 4.31 4.09 3.92 4.16 4.47 5.39
MD_WD 3.21 3.08 2.97 2.93 2.81 2.88 3.08 3.16 3.35
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