  Despite enjoying extensive applications in video analysis, three-dimensional
convolutional neural networks (3D CNNs)are restricted by their massive
computation and storage consumption. To solve this problem, we propose a
threedimensional regularization-based neural network pruning method to assign
different regularization parameters to different weight groups based on their
importance to the network. Further we analyze the redundancy and computation
cost for each layer to determine the different pruning ratios. Experiments show
that pruning based on our method can lead to 2x theoretical speedup with only
0.41% accuracy loss for 3DResNet18 and 3.28% accuracy loss for C3D. The
proposed method performs favorably against other popular methods for model
compression and acceleration.
