 In recent years, three-dimensional convolutional neural network (3D CNN) are intensively applied in the video analysis and action recognition and receives good performance. However, 3D CNN leads to massive computation and storage consumption, which hinders its deployment on mobile and embedded devices. In this paper, we propose a three-dimensional regularization-based pruning method to assign different regularization parameters to different weight groups based on their importance to the network. Our experiments show that the proposed method outperforms other popular methods in this area. In recent years, convolutional neural network (CNN) has developed rapidly and has achieved remarkable success in computer vision tasks such as identification, classification and segmentation. However, due to the lack of motion modeling, this image-based end-to-end feature can not directly apply to videos. In BID0 BID1 , the authors use three-dimensional convolutional networks (3D CNN) to identify human actions in videos. Tran et al. proposed a 3D CNN for action recognition which contains 1.75 million parameters BID2 . The development of 3D CNN also brings challenges because of its higher dimensions. This leads to massive computing and storage consumption, which hinders its deployment on mobile and embedded devices.In order to reduce the computation cost, researchers propose methods to compress CNN models, including knowledge distillation BID3 , parameter quantization BID4 BID5 , matrix decomposition BID6 and parameter pruning BID7 . However, all of the above methods are based on two-dimensional convolution. In this paper, we expand the idea of BID8 to 3D CNN acceleration. The main idea is to add group regularization items to the objective function and prune weight groups gradually, where the regularization parameters for different weight groups are differently assigned according to some importance criteria. In this paper, we implement the regularization based method for 3D CNN acceleration. By assigning different regularization parameters to different weight groups according to the importance estimation, we gradually prune weight groups in the network. The proposed method achieves better performance than other two popular methods in this area.
In this paper, we propose a three-dimensional regularization-based pruning method to accelerate the 3D-CNN.
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