【Pruning系列：一】Learning Efficient Convolutional Networks through Network Slimming

时间 2021-01-02

标签 Model Compression 计算机视觉深度学习神经网络卷积网络栏目系统网络繁體版

原文原文链接

NS 机构 Tsinghua,Intel motivation 训练中的剪枝基于BN（Batch Normalization）层的广泛使用，在BN层加入channel-wise scaling factor 并对之加L1 regularizer使之稀疏，然后裁剪scaling factor值小的部分对应权重用 BN 层的 y 来表示卷积核的重要程度，y 小对应卷积核重要性低 γ 非常小时，送入

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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. 模型剪枝----Learning Efficient Convolutional Networks through Network Slimming
9. Pruning Convolutional Neural Networks For Resource Efficient Inference
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1. 模型压缩：Networks Slimming-Learning Efficient Convolutional Networks through Network Slimming
2. 【Pruning系列：二】Learning Efficient Convolutional Networks through Network Slimming｜YOLOv3实践｜Pytorch 总结
3. 模型压缩之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. 模型剪枝----Learning Efficient Convolutional Networks through Network Slimming
9. Pruning Convolutional Neural Networks For Resource Efficient Inference
10. Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning

>>更多相关文章<<