High-parameter-efficiency convolutional neural networks

Currently, in order to deploy the convolutional neural networks (CNNs) on the mobile devices and address the over-fitting problem caused by the less abundant datasets, reducing the redundancy of parameters is the main target to construct the mobile CNNs. Based on this target, this paper proposes two novel convolutional kernels, multiple group reused convolutions (MGRCs) and decomposed point-wise convolutions (DPCs), to improve the efficiency of parameters by removing the parameter’s redundancy. The summation of MGRC and DPC is called high-parameter-efficiency convolutions (HPEC) in this paper, and the relevant CNNs can be called HPE-CNNs. Experimental results showed that, compared with the traditional convolutional kernels, HPEC can greatly decrease the model size without affecting the performance. Additionally, since the HPE-CNNs reduce the redundancy of parameters more thoroughly than the other mobile CNNs, they can address the over-fitting problem more effectively on the challenging datasets with less abundant training information.