论文品读：Learning both Weights and Connections for Efficient Neural Networks

时间 2020-12-23

标签模型压缩深度学习神经网络繁體版

原文原文链接

https://arxiv.org/abs/1506.02626v3 这是一篇关于模型压缩的15年文章，到目前为止（18年11月）有450的被引文章介绍了一种参数剪枝（weights pruning）方法，应该算是最基础的一种方法了，直接按照参数是否大于某个阈值来判断哪些参数是重要的，哪些参数是不重要。在不降低精度的前提下，在VGG-16上取得了13倍的参数压缩率，从138M个参数到10.8M

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1. 【Learning both Weights and Connections for Efficient Neural Networks】论文笔记
2. 《Learning both Weights and Connections for Efficient Neural Networks》论文笔记
3. 论文《Learning both Weights and Connections for Efficient Neural Network》阅读笔记
4. 网络模型剪枝-论文阅读《Learning both Weights and Connections for Efficient Neural Networks》
5. 【论文阅读】韩松《Efficient Methods And Hardware For Deep Learning》节选《Learning both Weights and Connections 》
6. 深度网络推理加速（Learning both Weights and Connections for Efficient Neural Networks）
7. 论文阅读——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
8. Neural Networks and Deep Learning-引论
9. 论文笔记系列-Simple And Efficient Architecture Search For Neural Networks
10. Neural Networks and Deep Learning -- Class 3: Shallow neural networks
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1. 【Learning both Weights and Connections for Efficient Neural Networks】论文笔记
2. 《Learning both Weights and Connections for Efficient Neural Networks》论文笔记
3. 论文《Learning both Weights and Connections for Efficient Neural Network》阅读笔记
4. 网络模型剪枝-论文阅读《Learning both Weights and Connections for Efficient Neural Networks》
5. 【论文阅读】韩松《Efficient Methods And Hardware For Deep Learning》节选《Learning both Weights and Connections 》
6. 深度网络推理加速（Learning both Weights and Connections for Efficient Neural Networks）
7. 论文阅读——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
8. Neural Networks and Deep Learning-引论
9. 论文笔记系列-Simple And Efficient Architecture Search For Neural Networks
10. Neural Networks and Deep Learning -- Class 3: Shallow neural networks

>>更多相关文章<<