Deep learning has become a ubiquitous technology to improve machine
intelligence. However, most of the existing deep models are structurally very
complex, making them difficult to be deployed on the mobile platforms with
limited computational power. In this paper, we propose a novel network
compression method called dynamic network surgery, which can remarkably reduce
the network complexity by making on-the-fly connection pruning. Unlike the
previous methods which accomplish this task in a greedy way, we properly
incorporate connection splicing into the whole process to avoid incorrect
pruning and make it as a continual network maintenance. The effectiveness of
our method is proved with experiments. Without any accuracy loss, our method
can efficiently compress the number of parameters in LeNet-5 and AlexNet by a
factor of $\bm{108}\times$ and $\bm{17.7}\times$ respectively, proving that it
outperforms the recent pruning method by considerable margins. Code and some
models are available at this https URL

本研究提出了一种名为动态网络手术的网络压缩方法，能够通过即时连接修剪显著降低网络复杂性，同时避免了错误裁剪的问题，并将其作为一个持续的网络维护过程，实验结果表明，该方法能够有效地压缩 LeNet-5 和 AlexNet 的参数数量，而不会损失精度，同时优于最近的裁剪方法。