A Novel Approach for Regularization of Convolutional Neural Network
Yuan Zhang, BiMing Shi
Journal of Digital Information Management, Vol. 15, No. 1, 2017.
DOI: 
Link: 
Abstract:
At present, the traditional convolutional
neural network (CNN) can easily cause overfitting in the
training process, thereby resulting in an invalid training
model. Thus, this study proposed a novel CNN
regularization method to avoid overfitting in the training
process and to increase the image classification accuracy
of CNN. The proposed method uses failure probability as
the theoretical basis. First, the failure probability density
(FPD) function of image pixel points was deduced, and
the FPD prediction model of pixel points in image
neighborhood was established. Second, this method
evaluated the FPD of image eigenvalues, selected the
pixel points with the minimum FPD in the image
neighborhood as the retention characteristics, and
extracted excellent features. Finally, this method
conducted a labeling classification experiment on three
image classification datasets (MNIST, CIFAR-10, and
CIFAR-100) and compared the image classification
accuracy of the three existing popular pooling methods
(Max, Average, and Stochastic pooling). Results
demonstrated that the proposed regularization method
based on FPD can increase the classification accuracy
of the image data. The classification accuracies gained
by the training datasets are all higher than 90%, and the
classification accuracies gained by the testing datasets
are all higher than 85%. The proposed CNN regularization
method possesses good generalization capability and
engineering applicability.