He Initialization, named after its creator Kaiming He, is a technique used to initialize the weights of neural networks. This method is particularly effective for layers using the Rectified Linear Unit (ReLU) activation functions, which are common in deep learning models.
The primary goal of weight initialization is to prevent the vanishing or exploding gradient problem, which can hinder the training of deep networks. When weights are initialized too small, the network may not learn effectively (vanishing gradients), and when they are too large, the gradients can become excessively large, leading to instability (exploding gradients).
He Initialization addresses these issues by setting the initial weights to random values drawn from a normal distribution with a mean of 0 and a variance of 2/n, where n is the number of input units in the layer. This scaling factor helps to maintain a balanced signal flow through the layers of the network, facilitating effective learning.
This method is especially beneficial for deep networks, as it allows for larger learning rates without the risk of gradient-related problems. By ensuring that the weights are appropriately scaled, He Initialization contributes to faster convergence during training and can lead to better overall performance of the neural network.
In summary, He Initialization is a vital technique in the field of deep learning that enhances the training of neural networks, particularly those using ReLU activations, by providing a robust strategy for weight initialization.