Pure max-plus/min-plus deep networks are shown to be the opposite of universal approximators, while inserting O(N)-parameter linear scalings per layer yields provably universal morphological networks.
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Training Deep Morphological Neural Networks as Universal Approximators
Pure max-plus/min-plus deep networks are shown to be the opposite of universal approximators, while inserting O(N)-parameter linear scalings per layer yields provably universal morphological networks.