Any Deep ReLU Network is Shallow

Villani, Mattia Jacopo, Schoots, Nandi

arXiv.org Artificial Intelligence 

We constructively prove that every deep ReLU network can be rewritten as a functionally identical three-layer network with weights valued in the extended reals. Based on this proof, we provide an algorithm that, given a deep ReLU network, finds the explicit weights of the corresponding shallow network. The resulting shallow network is transparent and used to generate explanations of the model's behaviour.

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