I am studying Andrew NG's lectures on Convolutional Neural Network and he had provided two reasons for CNNs having less parameters compared to Non-Convolutional networks . They are :
- Parameter Sharing
- Sparsity of connections .
While I can make sense of the first reason causing CNN to have less parameters . I don't understand why Sparsity of connections , that is , " Each output in a layer comes from a small number of inputs " cause the network to have less parameters .
Isn't the second reason a bit redundant ?
Can someone please explain ?