  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.
