Hi there, how are you doing?
I’m currently going through lesson 13 CNN. I have a quick question regarding to the kernel/feature filter of this network. I hope you could help me understand more about it.
My understanding of CNN so far is that the kernels values is randomly generated at the beginning of the training. It is then would be updated through training. Is it correct to say that the CNN model learn to “construct” the most effective feature maps/kernels so that important features of input could be extracted after convolution process?
On that same topic, let’s say 3 feature maps are randomly generated. Is it correct to say that it is possible for the 3 kernel to somehow end up with same exact values at the end of the training? If that was to happen, I assume that the output of each kernel would be exactly the same, which means the CNN model with 3 kernel would be just as effective as it is with 1 kernel?