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

I'm only 70% sure of what I say, so if it does not make sense, please edit or mention it before downvoting.

About location or spatial location: they mean the position of pixels in an image or feature map. A feature map is comparable to a sparse modified version of image where concepts are represented.

About so that different elements of the same feature map, at different locations, are normalized in the same way: some normalisation algorithms are local, so they are dependent of their close surrounding (location) and not the things far apart in the image. They probably mean that every pixel, regardless of their location, is treated just like the element of a set, independently of it's direct special surrounding.

About In Alg. 1, we let B be the set of all values in a feature map across both the elements of a mini-batch and spatial locations: They get a flat list of every values of every training example in the minibatch, and this list combines things whatever their location is on the feature map.