Xavier initialization

Information

• Xavier initialization, also known as Glorot initialization, is a weight initialization technique used in neural networks.
• The main goal of Xavier initialization is to set the initial weights of the network in such a way that the variance of the outputs of each layer is approximately equal to the variance of its inputs.
• The initialization is performed by randomly initializing the weights of the network using a uniform or normal distribution.
• The scale of the distribution is chosen based on the size of the input and output layers of each weight matrix.
• Specifically, the scale factor is chosen to be the square root of 2 divided by the sum of the number of inputs and outputs of each weight matrix.
• Xavier initialization is preferred over other weight initialization methods, such as random or uniform initialization, because it can speed up training and improve model performance.
• However, it may not work well with very deep networks or with certain activation functions, such as the ReLU function. In those cases, modifications of the Xavier initialization, such as He initialization, may be more appropriate.