Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reliable Identification of Redundant Kernels for Convolutional Neural
  Network Compression   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.

5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: stat
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
2
The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ph
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The categories of this paper are: q-bio