learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization论文

时间 2020-12-24

标签模型量化繁體版

原文原文链接

SQ-B(T)WN也是一种增量式量化算法，它从不同权重会导致不同量化误差角度，在近似实数权重时，逐渐增加量化比例最终完成全部权重量化。训练时分阶段对权重量化，都是瞄准量化造成误差的一个量化时机问题通过逐步量化权重，比直接全部量化产生的更加合适梯度方向从算法结果上来说，看起来并不是特别好。3bits 时没有同样增量训练的INQ好

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1. Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
2. Stochastic Computing + Quantization
3. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】论文笔记
4. Neural Networks and Deep Learning-引论
5. 论文ImageNet Classification with Deep Convolutional Neural Networks（Alexnet2012）
6. Ternary Neural Networks with Fine-Grained Quantization
7. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
8. Few-Shot Learning with Graph Neural Networks
9. LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
10. deep compression:compressing deep neural networks with pruning,trained quantization and huffman codi
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1. Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
2. Stochastic Computing + Quantization
3. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】论文笔记
4. Neural Networks and Deep Learning-引论
5. 论文ImageNet Classification with Deep Convolutional Neural Networks（Alexnet2012）
6. Ternary Neural Networks with Fine-Grained Quantization
7. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
8. Few-Shot Learning with Graph Neural Networks
9. LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
10. deep compression:compressing deep neural networks with pruning,trained quantization and huffman codi

>>更多相关文章<<