An Expansion Convolution Method Based on Local Region Parameter Sharing In this paper, a new convolution method for convolutional neural networks (CNNs) is proposed to improve the accuracy of image classification. To contain more efficient context, some of the parameters in the kernel are selectively expanded so as to be shared by the surrounding pixels. Thus, the convolution filter is enlarged meanwhile the number of the parameters is not increased. Compared to the traditional methods, the proposed method can restrain the overfitting problem well. The experimental results on benchmarks show that the proposed method can achieve higher accuracies closed to the deeper networks, and get better accuracies in the case of the same network depth.

FunctionallyPredefined Kernel: a Way to Reduce CNN Computation Convolutional Neural Networks (CNNs) have achieved high classification accuracy in image recognition, and now, they are widely used for numerous applications. For higher accuracy or more advanced applications, CNNs need to consume tremendous computational resources and time. Hence, many studies for reducing the computational cost of CNNs are actively being conducted. However, many previous methods for reducing the computational cost lead to a nonnegligible loss in output accuracy. Therefore, it is still a challenge to reduce the computational cost of CNNs with keeping the output accuracy high. In this paper, we propose a novel concept FunctionallyPredefined Kernel to reduce the computational cost for CNN training and discuss the potential of computation reuse to reduce the computational cost for CNN inference. Our experimental results show that the number of parameters to be trained can be significantly reduced by utilizing FunctionallyPredefined Kernels without accuracy loss. In addition, we revealed that CNNxe2x80x99s inference process includes many convolution operations with the same inputs and computation reuse, therefore, has high affinity to CNN computation.