Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convolutional Neural Network Compression through Generalized Kronecker
  Product Decomposition   Modern Convolutional Neural Network (CNN) architectures, despite their
superiority in solving various problems, are generally too large to be deployed
on resource constrained edge devices. In this paper, we reduce memory usage and
floating-point operations required by convolutional layers in CNNs. We compress
these layers by generalizing the Kronecker Product Decomposition to apply to
multidimensional tensors, leading to the Generalized Kronecker Product
Decomposition (GKPD). Our approach yields a plug-and-play module that can be
used as a drop-in replacement for any convolutional layer. Experimental results
for image classification on CIFAR-10 and ImageNet datasets using ResNet,
MobileNetv2 and SeNet architectures substantiate the effectiveness of our
proposed approach. We find that GKPD outperforms state-of-the-art decomposition
methods including Tensor-Train and Tensor-Ring as well as other relevant
compression methods such as pruning and knowledge distillation.

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The categories of this paper are: nlin
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The categories of this paper are: supr-con
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The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: eess
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The categories of this paper are: q-bio
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The categories of this paper are: gr-qc
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The categories of this paper are: physics
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: patt-sol