Kaiming Initialization, or He Initialization, is an initialization method for neural networks that takes into account the non-linearity of activation functions, such as ReLU activations.

A proper initialization method should avoid reducing or magnifying the magnitudes of input signals exponentially. Using a derivation they work out that the condition to stop this happening is:

$$\frac{1}{2}n_{l}\text{Var}\left[w_{l}\right] = 1 $$

This implies an initialization scheme of:

$$ w_{l} \sim \mathcal{N}\left(0, 2/n_{l}\right)$$

That is, a zero-centered Gaussian with standard deviation of $\sqrt{2/{n}_{l}}$ (variance shown in equation above). Biases are initialized at $0$.

Source: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification


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