模型压缩：Networks Slimming-Learning Efficient Convolutional Networks through Network Slimming
Network Slimming-Learning Efficient Convolutional Networks through Network Slimming（Paper） 2017年ICCV的一篇paper，思路清晰，骨骼清奇~~ 创新点： 1. 利用batch normalization中的缩放因子γ 作为重要性因子，即γ越小，所对应的channel不太重要，就可以裁剪（pruning
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相关文章
- 1. 模型压缩：Networks Slimming-Learning Efficient Convolutional Networks through Network Slimming
- 2. 模型压缩之Learning Efficient Convolutional Networks through Network Slimming
- 3. 模型剪枝----Learning Efficient Convolutional Networks through Network Slimming
- 4. 《Learning Efficient Convolutional Networks through Network Slimming》论文笔记
- 5. 【论文阅读笔记】Learning Efficient Convolutional Networks through Network Slimming
- 6. DL经典文章翻译2：Learning Efficient Convolutional Networks through Network Slimming
- 7. [论文理解] Learning Efficient Convolutional Networks through Network Slimming
- 8. 【Pruning系列：一】Learning Efficient Convolutional Networks through Network Slimming
- 9. 【Pruning系列：二】Learning Efficient Convolutional Networks through Network Slimming｜YOLOv3实践 ｜Pytorch 总结
- 10. 模型压缩