Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Network Compression using Transform Coding and Clustering   With the deployment of neural networks on mobile devices and the necessity of
transmitting neural networks over limited or expensive channels, the file size
of the trained model was identified as bottleneck. In this paper, we propose a
codec for the compression of neural networks which is based on transform coding
for convolutional and dense layers and on clustering for biases and
normalizations. By using this codec, we achieve average compression factors
between 7.9-9.3 while the accuracy of the compressed networks for image
classification decreases only by 1%-2%, respectively.

14
The categories of this paper are: math-ph
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph