I have two question about convolutional neural networks. The first:
after we do a layer of convolution and get a set of feature maps are the next convolutional layer filters applied distinctly for every feature map? Is there ever a moment when different feature map are mixed in some way before the fully connected layers? In my understanding every feature map learn a particular feature so for example one map fins horizontal lines and another vertical lines, so it would make sense that an higher order feature map would like to integrate both information from vertical and horizontal line to learn higher level feature.
I understood how the filter of a convolutional layer could be 3 dimensional since the input could be 3 dimensional, but after you apply the 3 dimensional filter you get 2 dimensional feature map, does it mean that other than the first convolutional layer all the others contain just 2 dimensional filters?
Are subsequent 3d filters the answer to the first question? since they actually get information from multiple feature map but if this is the case the ordering of the feature maps would have an influence on which of them get integrated together and which not.