# Neural Network Compression using Transform Coding and Clustering

Source: [Neural Network Compression using Transform Coding and Clustering](<https://www.sota2.com/research/paper/68fa64651e209131cea04684>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:03:11.991Z

Authors: Thorsten Laude, Yannick Richter, J\\"orn Ostermann

Published: 2018-05-18T14:57:52.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1805.07258>)

[PDF](<https://arxiv.org/pdf/1805.07258.pdf>)