Soft Weight-Sharing for Neural Network Compression

Karen Ullrich, Edward Meeds, Max Welling

Abstract:

The success of deep learning in numerous application domains created the desire to run and train them on mobile devices. This however, conflicts with their computationally, memory and energy intense nature, leading to a growing interest in compression.
Recent work by Han et al. (2016) propose a pipeline that involves retraining, pruning and quantization of neural network weights, obtaining state-of-the-art compression rates.
In this paper, we show that competitive compression rates can be achieved by using a version of "soft weight-sharing" (Nowlan & Hinton, 1991). Our method achieves both quantization and pruning in one simple (re-)training procedure.
This point of view also exposes the relation between compression and the minimum description length (MDL) principle.

TL;DR: We use soft weight-sharing to compress neural network weights.

Conflicts: uva.nl

Keywords: Deep learning, Optimization

Community Implementations: [ 4 code implementations](https://www.catalyzex.com/paper/arxiv:1702.04008/code)