I am having trouble understanding the way 2 or more convolutional layers (each followed by a pooling layer) work in a CNN.
Consider the input to be a 3 channel 300x300 image. If the first convolution layer has 32 convolutions and the second layers have 64 convolutional layers, then the first layer creates 32 feature maps. But how many feature maps does the second layer create? Does every convolution out of 64 act on the previously generated 32 feature maps, …