《FPGM：Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration》论文笔记
参考代码：filter-pruning-geometric-median 1. 概述 导读：在之前的网络剪枝文章中一般将网络filter的范数作为其重要性度量，范数值较小的代表的filter越不重要，将其从网络中裁剪掉，反之也就越重要。这篇文章分析了这一类型的网络剪枝算法，并指出这类方法并不是很高效，因为这一类的方法如果要较为成功的使用所依赖的两点并不是很容易满足： 1）filter的范数偏差应该
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相关文章
- 1. 论文笔记：Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
- 2. 《Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks》论文笔记
- 3. Global Sparse Momentum SGD for Pruning Very Deep Neural Networks 论文阅读笔记
- 4. Channel Pruning for Accelerating Very Deep Neural Networks
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- 10. Learning Convolutional Neural Networks for Graphs论文笔记