Weighted-Entropy-based Quantization for Deep Neural Networks
論文：Weighted-Entropy-based Quantization for Deep Neural Networks 論文鏈接 這是CVPR2017的一篇文文章，是用基於weight entropy做深度學習模型quantization的模型壓縮和加速算法。 深度學習模型壓縮和加速是近年來的一個研究熱點，也是將深度學習模型部署到移動端的必經之路，一方面要壓縮模型的大小，減少存儲，另一方面
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相關文章
- 1. Weighted-Entropy-based Quantization for Deep Neural Networks
- 2. Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
- 3. LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
- 4. Deep Neural Networks for Object Detection
- 5. Aggregated Residual Transformations for Deep Neural Networks
- 6. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】論文筆記
- 7. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 8. deep compression:compressing deep neural networks with pruning,trained quantization and huffman codi
- 9. Ternary Neural Networks with Fine-Grained Quantization
- 10. Improving Deep Neural Networks