The term He initialization refers to the first author in the paper “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification”.

He initialization initializes the bias vectors of a neural network to \(0\) and the weights to random numbers drawn from a Gaussian distribution where the mean is \(0\) and the variance is \(\sqrt(2/n_l)\) where \(n_l\) is the dimension of the previous layer.