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the relationship between the number of filters/kernels and the number of feature maps
The above image is from "Deep Learning Tutorial" by Yann LeCun and Marc'Aurelio Ranzato (see pages 73 and 81).
I don't understand why 64 kernels (from the input to layer 1) produce 64 feature maps while 4096 kernels (layer 2 to layer 3) give 256 feature maps.