Trained Ternary Quantization
本文是在TWQ(Ternary Weight Quantization)的基礎上做出的改進，其流程可以簡單概括爲： 首先將全精度權重正則化到[-1,1]之間，然後根據一個每層相同的閾值t來進行量化(這裏使用統一的t是爲了縮小搜索空間，用每個通道不同的t應該能得到更好的表現，但感覺會增加過多的計算消耗)爲-1，0和1，最後分別乘以Wn和Wp作爲結果計算loss，再通過梯度下降更新Wn、Wp和原始梯度
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相關文章
- 1. TRAINED TERNARY QUANTIZATION 論文
- 2. 三值網絡--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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