Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
本文提出了一種使用隨機算法量化網絡的方法。 它的思路類似INQ，都是將權重劃分爲被量化的和全精度部分，然後逐次增加量化的比例，直到百分百。不同之處在於量化權重的選取：INQ是按照從大到小的順序進行量化，而本文則根據量化誤差的大小選取： 這裏Wi是某通道權重的全精度值，Qi是其量化後的值。作者定義一個函數： 作者依據該函數提出了四種量化方案： 1：量化概率爲1/m，m爲權重的通道數。 2：線性函數
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相關文章
- 1. learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization論文
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