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.

本文提出了一种基于三维正则化神经网络剪枝的方法，通过分配不同的正则化参数来减少计算和存储消耗，通过分析每个层的冗余和计算成本来确定不同的剪枝比例，实验表明，基于我们的方法的剪枝可以为 3DResNet18 和 C3D 等模型带来 2 倍的理论加速，并仅出现 0.41％和 3.28％的准确度损失，该方法在模型压缩和加速方面表现优异。