Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image

2.2 脉冲转换 ANN 操作

2.2.1 转换偏置

偏置在ANN中是标准的，但是在转换前的SNN是需要去掉的。

2.2.2 参数归一化

一个复杂问题的根源在于SNN的模拟时间步，神经元的放电速率被限制在[0,rmax][0,r_{max}][0,rmax],然而，在典型的ANN中是没有这个限制的。权重正则化是一种手段，去防止类似的错误：太低或者太高的放电率。

2.2.2.1 偏置的正则化

假设最大的激活值是ReLU在第lll层是λl\lambda^lλl,那么权重和偏置应该按照如下的规则进行正则化：
Wl→Wlλl−1/λlW^l\rightarrow W^l\lambda^{l-1}/\lambda^lWl→Wlλl−1/λl
bl→bl/λlb^l\rightarrow b^l/\lambda^lbl→bl/λl

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Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image

2.2 脉冲转换 ANN 操作

2.2.1 转换偏置

2.2.2 参数归一化

2.2.2.1 偏置的正则化

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