One of the most common initialization techniques in training a DNN is random initialization. The idea of using random initialization is just sampling each weight from a normal distribution of the input dataset with low deviation. Well, a low deviation allows you to bias the network towards the simple 0 solutions.
But what does it mean? The thing is that, the initialization can be completed without the bad repercussions of actually initializing the weights to 0. Secondly, Xavier initialization is often used to train CNNs. It is similar to random initialization but often turns out to work much better. Now let me explain the reason for this:
- Imagine that you initialize the network weights randomly but they turn ...
