模型加速--CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization

CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization
CVPR2018
http://www.sfu.ca/~ftung/
裁剪和量化一体化框架

本文的思路比较简单，裁剪+量化一体训练模型分三个步骤：
1） Clipping 裁剪，将网络中的权重系数值接近0 的权重全部置零，当然这种置零是临时性的，后面的训练迭代根据实际情况调整。这里的阈值自适应确定，（model the objective function as a Gaussian process）
2）Partitioning 切分， partition the non-clipped portion of the 1-D axis of weight values into quantization intervals，这里我们使用了 linear (uniform) partitioning ，也可以使用其他自适应切分如 weighted entropy
3）Quantizing 量化 update the quantization levels the discrete values that the weights are permitted to take in the compressed network

Experiments

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模型加速--CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization

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