【Learning both Weights and Connections for Efficient Neural Networks】論文筆記
追隨Song Han大神的第一篇網絡壓縮論文（NIPS’15），論文鏈接：https://arxiv.org/abs/1506.02626 這篇論文只是簡單介紹了裁剪的思路，並沒有涉及到網絡加速。 效果： 作者用了4個網絡實驗 Lenet-300-100, pruning reduces the number of weights by 12× Lenet-5, pruning reduces t
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相關文章
- 1. 《Learning both Weights and Connections for Efficient Neural Networks》論文筆記
- 2. 論文品讀：Learning both Weights and Connections for Efficient Neural Networks
- 3. 論文《Learning both Weights and Connections for Efficient Neural Network》閱讀筆記
- 4. 網絡模型剪枝-論文閱讀《Learning both Weights and Connections for Efficient Neural Networks》
- 5. 【論文閱讀】韓鬆《Efficient Methods And Hardware For Deep Learning》節選《Learning both Weights and Connections 》
- 6. 深度網絡推理加速（Learning both Weights and Connections for Efficient Neural Networks）
- 7. 論文筆記系列-Simple And Efficient Architecture Search For Neural Networks
- 8. Learning Convolutional Neural Networks for Graphs論文筆記
- 9. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】論文筆記
- 10. 【論文閱讀筆記】Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference