I am trying to map my basic understanding of MLP's to CNN's. Why does a CNN sacrifice all negative inputs with the ReLU over the sigmoid. Is it because:
The sigmoid has a range of between zero and 1 which is worse for CNN's than 0 to infinity.
Is it to do with the fact the function only has one horizontal asymptote?
The negative inputs are meaningless? Or to speed up computation? Implied here on this question.
Any help is a appreciated.