Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extreme Network Compression via Filter Group Approximation   In this paper we propose a novel decomposition method based on filter group
approximation, which can significantly reduce the redundancy of deep
convolutional neural networks (CNNs) while maintaining the majority of feature
representation. Unlike other low-rank decomposition algorithms which operate on
spatial or channel dimension of filters, our proposed method mainly focuses on
exploiting the filter group structure for each layer. For several commonly used
CNN models, including VGG and ResNet, our method can reduce over 80%
floating-point operations (FLOPs) with less accuracy drop than state-of-the-art
methods on various image classification datasets. Besides, experiments
demonstrate that our method is conducive to alleviating degeneracy of the
compressed network, which hurts the convergence and performance of the network.

31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph