could not calculate the dimensions after pooling and subsampling in the famous Convolutional neural nets example

Question

The above image is from a pdf by Yann LeCun, titled "Hierarchical Models Of Perception and Reasoning"

I am not able to understand the how the layer 2 is 14X14 feature maps? How can 75X75 matrix with 10X10 pooling and 5X5 subsampling gives 14X14 matrix ?

Answer 1

If you refer to this other paper by LeCun et al. an identical network is used with a larger input (143x143 grayscale image):

The first stage has 64 filters of size 9x9, followed by a subsampling layer with 5x5 stride, and 10x10 averaging window. [...]

This gives the right dimension:

output size = (input size - window size) / step + 1
            = (75-10) / 5 + 1
            = 14