  To compress deep convolutional neural networks (CNNs) with large memory
footprint and long inference time, this paper proposes a novel pruning
criterion using layer-wised Ln-norm of feature maps. Different from existing
pruning criteria, which are mainly based on L1-norm of convolution kernels, the
proposed method utilizes Ln-norm of output feature maps after non-linear
activations, where n is a variable, increasing from 1 at the first convolution
layer to inf at the last convolution layer. With the ability of accurately
identifying unimportant convolution kernels, the proposed method achieves a
good balance between model size and inference accuracy. The experiments on
ImageNet and the successful application in railway surveillance system show
that the proposed method outperforms existing kernel-norm-based methods and is
generally applicable to any deep neural network with convolution operations.
