CNNs: What happens from one neuron volume to the next?

Question

I've gone through several descriptions of CNNs online and they all leave out a crucial part as if it were trivial.

A "volume" of neurons consists of several parallel layers ("feature maps"), each the result of convolving with a different kernel.

Between volumes, there is usually a step where layers are pooled and subsampled.

The next volume has a different number of parallel layers.

How do the feature maps from one volume connect to the feature maps of the next volume? Is it one-to-many? Many-to-many? Do N kernels apply to each of the M feature maps in the first volume, yielding N*M feature maps in the second volume? Are these N kernels the same for each feature map in the first volume, or do different kernels apply to each one?

Or, is the number of maps in the second volume not necessarily a multiple of the number in the first volume? If so, do maps in the first volume get cross-synthesized somehow? Or, maybe different numbers of maps in the second volume follow from each one in the first?

Or, is it some other of umpteen trillion possibilities?

Answer 1

Short answer: One to many
Long answer:

The point is that you use a 3D convolution in a CNN. Each kernel has the size of n*m*C (C is the number of feature maps) and every feature map has its own kernel(=weights) and bias.

An example:

The size of layer 2 is 9x9x10 (stride 1, no padding), the kernel size is 3x3x10.

The dimension of the next layer would be 3x3xn (n are the number of kernels that layer 2 has).

Here I found a very clear explanation.

I hope this helps.

Answer 2

If you really want to understand what goes on in a CNN and visualize it, then you can give a read to this.

This course needs some prerequisites like having a certain level of idea of the forward and backward propagation and knowing how the network learns on itself.

CNN is just a way of moving from fully connected layers (every input connects to every output) to partially connected layers.