【论文】（论文+代码）软权值共享的神经网络压缩技术

论文《Soft Weight-Sharing for Neural Network Compression》摘要：

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.

论文链接：

https://arxiv.org/abs/1702.04008

代码链接：

https://github.com/KarenUllrich/Tutorial-SoftWeightSharingForNNCompression

原文链接：

http://weibo.com/1402400261/EvJI61Oct?from=page_1005051402400261_profile&wvr=6&mod=weibotime&type=comment#_rnd1487233793944