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arXiv · 1702.04008

Soft Weight-Sharing for Neural Network Compression

Karen Ullrich·Edward Meeds·Max Welling
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Abstract

The success of deep learning in numerous application domains created the de- sire 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. (2015a) 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, 1992). 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.

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BibTeXRIS

Karen Ullrich, Edward Meeds, Max Welling. 2017-02-13. Soft Weight-Sharing for Neural Network Compression. https://arxiv.org/abs/1702.04008

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