Pruning convolutional neural networks for resource efficent inference
这是篇2017年ICLR的会议，文章提出了一种基于泰勒展开的网络剪枝方法。文章认为，这里一类的剪枝方法一般分为三类： 一个已经训练好的网络 基于一定的准则在修剪和微调反复 适时地停止 所以，这个修剪的“准则”就十分的重要，文章列出了一些以往的准则： Minimun weight：训练时加入L1或L2正则化，并修剪权值小于某一阈值的权重 Activation：修剪经激活层后与small acti
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相关文章
- 1. Pruning Convolutional Neural Networks For Resource Efficient Inference
- 2. Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
- 3. Coarse pruning of convolutional neural networks with random masks
- 4. Channel Pruning for Accelerating Very Deep Neural Networks
- 5. 《FPGM：Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration》论文笔记
- 6. 论文笔记：Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
- 7. 《Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks》论文笔记
- 8. Understanding Convolutional Neural Networks for NLP
- 9. Convolutional Neural Networks for Sentence Classification
- 10. Learning Convolutional Neural Networks for Graphs