LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks

本文提出了一种通过学习来获得合适的量化间隔的方法。

本文的思路很简单，作者将uniform量化视作几个量化level的线性叠加：

左边是量化level，右边是线性叠加系数，它的取值范围为0或1。

于是作者提出类似的结构：

v是一个浮点数，e是线性叠加系数，它的取值范围为-1或1。

其优化目标为：

训练方法是分别固定B和v中的一个进行训练，这也是同时训练的参数过多时常常采用的一种方法。

我觉得这篇文章不太行，因为它使用了浮点数进行量化。如果说后续再量化到int(然后作者并没有这样做)，那么会带来误差；如果一直使用浮点数，那么它量化的程度就非常有限。

不过这篇文章将uniform量化视作不同量化level的线性组合的思想还是挺有趣的。

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LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks

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