Actually, there's already a better way to perform initialization, based on the so called lottery ticket hypothesis [1]. I haven't gotten to the article, so I'll just regurgitate the abstract, but basically there frequently are subnetworks which may be exposed by pruning trained networks which perform on par with full sizes neural nets with ≈20% of the parameters and substantially quicker training time. It turns out that with some magic algorithm described in the paper, one can initialize weights to quickly find these "winning tickets" to drastically reduce neural network size and training time.