This is a simple question. I know the weights in a neural network can be initialized in many different ways like: random uniform distribution, normal distribution, and Xavier initialization. But what is the weight initialization trying to achieve?

Is it trying to allow the gradients to be large so it can quickly converge? Is it trying to make sure there is no symmetry in the gradients? Is it trying to make the outputs as random as possible to learn more from the loss function? Is it only trying to prevent exploding and vanishing gradients? What would the perfect weights (without being learned parameters) for a problem achieve? What makes them perfect? What are the properties in an initialization that makes the network learn faster?

This is a simple question. I know the weights in a neural network can be initialized in many different ways like: random uniform distribution, normal distribution, and Xavier initialization. But what is the weight initialization trying to achieve?

Is it trying to allow the gradients to be large so it can quickly converge? Is it trying to make sure there is no symmetry in the gradients? Is it trying to make the outputs as random as possible to learn more from the loss function? Is it only trying to prevent exploding and vanishing gradients? Is it more about speed or finding a global maximum? What would the perfect weights (without being learned parameters) for a problem achieve? What makes them perfect? What are the properties in an initialization that makes the network learn faster?