【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】论文笔记
追随Song Han大神的第二篇网络压缩论文（ICLR’16），论文链接：https://arxiv.org/abs/1510.00149 这篇论文是【Learning both Weights and Connections for Efficient Neural Networks】的升级版 效果： 大概能实现35-49倍的压缩。 在ImageNet数据集上，将AlexNet所需的存储量从24
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相关文章
- 1. Deep Compression : Compressing Deep Neual Networks With Pruning, Trained Quantization And Huffman Co
- 2. 2018-22Deep Compression: Compression Deep Neural Networks With Pruning, Trained Quantization And...
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- 4. [deep compression] compressing deep neural networks 论文学习
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- 6. 【Comprssion】《Deep Compression：Compressing Deep Neural Networks with Pruning，Trained QuantizationXXX》
- 7. 【论文阅读笔记】Deep Neural Network Compression with Single and Multiple Level Quantization
- 8. [论文解读] Deep Compression
- 9. 深度学习（六十九）darknet 实现实验 Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffma
- 10. Channel Pruning for Accelerating Very Deep Neural Networks 论文笔记