This repository demonstrates diminishing input signal variance when using the Kaiming He weight initialization method. You can read about this in more detail here.

When using the He approach, the featurewise variances diminish as the network depth increases:

The feature statistics are distributed the following way at the output layer:

This is an interesting result because the paper promises variance preserving behavior.