Fully shared convolutional neural networks

Recently, the group convolutions are widely used in mobile convolutional neural networks (CNNs) to improve the model’s efficiency. However, the training process of these popular group-conv mobile models is usually time-consuming compared to the regular models. Since some practical applications usually need retraining when the new data are added, the training process should be also efficient. Therefore, this paper firstly explores the inefficiency of current popular group-conv mobile CNNs to propose the guidelines of constructing mobile CNNs. According to these guidelines, the fully shared convolutional neural networks (FSC-Nets) are proposed in this paper. The FSC-Nets can share the same filter in both the channel and layer extents, and it is the first method to share the filters from all the extents. Experimental results show that, compared with current popular regular networks and the latest state-of-the-art group-conv mobile networks, the FSC-Nets can perform better while effectively decreasing the model size and runtime in both the training and test processes.