（筆記）細顆粒度的三值網絡Ternary Neural Networks with Fine-Grained Quantization
原文鏈接 摘要 方法 實驗效果 原文鏈接 https://arxiv.org/abs/1705.01462 摘要 提出一種三值化方法，激活函數是8/4bit的，不需要訓練，對N個權重量化到三值 ，N=2,4,8,16等等。當N=4時，ResNet-101和ResNet-50的Top1準確率分別下降3.7%和4.2%。 方法 目的：不訓練地，把32bit浮點權重 W W 量化爲 {−α,0,α} {
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相關文章
- 1. Ternary Neural Networks with Fine-Grained Quantization
- 2. 三值網絡--Trained Ternary Quantization
- 3. Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
- 4. 二值網絡--TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights
- 5. （筆記）網絡壓縮量化，訓練三值量化TRAINED TERNARY QUANTIZATION
- 6. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】論文筆記
- 7. Trained Ternary Quantization
- 8. Compression Deep Neural Networks With Pruning, Trained Quantization And Huffman Coding
- 9. 2018-22Deep Compression: Compression Deep Neural Networks With Pruning, Trained Quantization And...
- 10. Ternary weight networks 論文筆記