A Fully Rigorous Proof of the Derivation of Xavier and He's Initialization for Deep ReLU Networks

This work addresses the need for rigorous theoretical justification in deep learning initialization, though it is incremental as it formalizes existing methods rather than introducing new ones.

The paper provides a fully rigorous proof for deriving Xavier and He's initialization methods specifically for deep ReLU networks, establishing a solid theoretical foundation for these widely used techniques.