《Learning both Weights and Connections for Efficient Neural Networks》论文笔记
1. 论文思想 深度神经网络在计算与存储上都是密集的，这就妨碍了其在嵌入式设备上的运用。为了解决该问题，便需要对模型进行剪枝。在本文中按照网络量级的排序，使得通过只学习重要的网络连接在不影响精度的情况下减少存储与计算量。论文中的方法分为三步：首先，使用常规方法训练模型；使用剪枝策略进行模型修剪；在修剪模型的基础上进行finetune。经过试验证明改文章提出的方法使得AlexNet的大小减小了9倍，
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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