Batch Normalization in Convolutional Neural Network

Question

I am newbie in convolutional neural networks and just have idea about feature maps and how convolution is done on images to extract features. I would be glad to know some de

Answer 1

1. Firstly we need to make it clear that the depth of a kernel is determined by previous feature map's channel num, and the number of kernel in this layer determins the channel num of next feature map (the next layer).
2. then we should make it clear that each kernel(three dimentional usually) will generate just one channel of feature map in the next layer.
3. thirdly we should try to accept the idea of each points in the generated feature map (regardless of their position) are generated by the same kernel, by sliding on previous layer. So they could be seen as a distribution generated by this kernel, and they could be seen as samples of a stochastic variable. Then they should be averaged to obtain the mean and then the variance. (it not rigid, only helps to understand) This is what they say "so that different elements of the same feature map, at different locations, are normalized in the same way"