The output of layer S2 has dimension: 10x10x6 - so basically an image with 6 convultions applied to it to derive features.
If each dimension was again submitted to 6 filters the resulting output would be of 10x10x36 however it is 10x10x16. Initially I stumble on it but finnaly I udnerstood that this is done be combining inputs from layer S2 and applying one kernel on it as it's explained in the article:
Layer C3 is a convolutional layer with 16 feature maps Each unit in each feature map is connected to several 5x5 neighborhoods at identical locations in a subset of S2s feature maps
The rationale behind the connection scheme in table I is the following The 1rst six C3 feature maps take inputs from every contiguous subsets of three feature maps in S2. The next six take input from every contiguous subset of four. The next three take input from some discontinuous subsets of four Finally the last one takes input from all S2 feature maps Layer C3 has 1,516 trainable parameters and 151,600 connections
What I am still not uderstand is how exactly should I combine them?
In previous layer I've just applied 6 kernels on 1 dimension, resulting in 6 dimensions what was understandable. Here I am a bit lost to be honest :(
Please help. Mateusz