【Learning both Weights and Connections for Efficient Neural Networks】论文笔记
追随Song Han大神的第一篇网络压缩论文（NIPS’15），论文链接：https://arxiv.org/abs/1506.02626 这篇论文只是简单介绍了裁剪的思路，并没有涉及到网络加速。 效果： 作者用了4个网络实验 Lenet-300-100, pruning reduces the number of weights by 12× Lenet-5, pruning reduces t
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相关文章
- 1. 《Learning both Weights and Connections for Efficient Neural Networks》论文笔记
- 2. 论文品读：Learning both Weights and Connections for Efficient Neural Networks
- 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. 论文笔记系列-Simple And Efficient Architecture Search For Neural Networks
- 8. Learning Convolutional Neural Networks for Graphs论文笔记
- 9. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】论文笔记
- 10. 【论文阅读笔记】Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference