## 论文解读：Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights (INQ)

### 亮点1：量化表征方式 variable-length encoding scheme

$scale=max(abs(W))$

$\alpha=(2^{bit-1}-1) \div scale$

$W=round(W \times \alpha) \div \alpha$

$n_{1} = floor(log{2}(\frac{4scale}{3}))$

$n_{2}=n_{1}+1-2^{bit-2}$

$n_{2} \leq n_{1}$

### 亮点2：渐进式量化的策略 weight partition, group-wise quantization and re-training

Learning both weights and connections for efficient neural networks (Han et al., 2015)

Dynamic network surgery for efficient dnns (Guo et al., 2016)

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