Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
本文提出了一种使用随机算法量化网络的方法。 它的思路类似INQ，都是将权重划分为被量化的和全精度部分，然后逐次增加量化的比例，直到百分百。不同之处在于量化权重的选取：INQ是按照从大到小的顺序进行量化，而本文则根据量化误差的大小选取： 这里Wi是某通道权重的全精度值，Qi是其量化后的值。作者定义一个函数： 作者依据该函数提出了四种量化方案： 1：量化概率为1/m，m为权重的通道数。 2：线性函数
相关文章
- 1. learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization论文
- 2. Stochastic Computing + Quantization
- 3. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】论文笔记
- 4. Ternary Neural Networks with Fine-Grained Quantization
- 5. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 6. LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
- 7. Few-Shot Learning with Graph Neural Networks
- 8. deep compression:compressing deep neural networks with pruning,trained quantization and huffman codi
- 9. Neural Networks and Deep Learning
- 10. Deep Compression : Compressing Deep Neual Networks With Pruning, Trained Quantization And Huffman Co
- 更多相关文章...
- • XSLT
- • XQuery 添加元素 和属性 - XQuery 教程
- • Java Agent入门实战（一）-Instrumentation介绍与使用
- • Java Agent入门实战（三）-JVM Attach原理与使用
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization论文
- 2. Stochastic Computing + Quantization
- 3. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】论文笔记
- 4. Ternary Neural Networks with Fine-Grained Quantization
- 5. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 6. LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
- 7. Few-Shot Learning with Graph Neural Networks
- 8. deep compression:compressing deep neural networks with pruning,trained quantization and huffman codi
- 9. Neural Networks and Deep Learning
- 10. Deep Compression : Compressing Deep Neual Networks With Pruning, Trained Quantization And Huffman Co