Trained Ternary Quantization
本文是在TWQ(Ternary Weight Quantization)的基础上做出的改进，其流程可以简单概括为： 首先将全精度权重正则化到[-1,1]之间，然后根据一个每层相同的阈值t来进行量化(这里使用统一的t是为了缩小搜索空间，用每个通道不同的t应该能得到更好的表现，但感觉会增加过多的计算消耗)为-1，0和1，最后分别乘以Wn和Wp作为结果计算loss，再通过梯度下降更新Wn、Wp和原始梯度
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相关文章
- 1. TRAINED TERNARY QUANTIZATION 论文
- 2. 三值网络--Trained Ternary Quantization
- 3. Ternary Neural Networks with Fine-Grained Quantization
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- 5. Deep Compression : Compressing Deep Neual Networks With Pruning, Trained Quantization And Huffman Co
- 6. 2018-22Deep Compression: Compression Deep Neural Networks With Pruning, Trained Quantization And...
- 7. A perceptual quantization strategy for HEVC based on a convolutional neural network trained on natur
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- 9. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】论文笔记
- 10. deep compression:compressing deep neural networks with pruning,trained quantization and huffman codi