池化(Pooling):其作用在于(1)对一些小的形态改变保持不变性,Invariance to small transformations;(2)拥有更大的感受域,Larger receptive fields。pooling的方式有sum or max。
Normalization:Equalizes the features maps。它的作用有:(1) Introduces local competition between features;(2)Also helps to scale activations at each layer better for learning;(3)Empirically, seems to help a bit (1–2%) on ImageNet