模型压缩之Learning Efficient Convolutional Networks through Network Slimming
1. 摘要 在许多现实应用中，深度神经网络的部署因为其高额的计算成本变得很困难。在这篇论文中，咱们提出了一种新颖的CNNs学习方案，它同时知足：html 减少模型大小； 减小运行时的内存占用； 在不影响精度的同时，下降计算操做数； 利用通道稀疏化这样一个简单但却有效的方法，上面的条件很容易得到。不一样于许多已经存在的方法，咱们的方法能够直接应用到现代CNN网络架构中，在训练过程引入最小的花销，并且
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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