If I got well the idea of dropout, it allows improving the sparsity of the information that comes from one layer to another by setting some weights to zero.
On the other hand, pooling, let's say max-pooling, takes the maximum value in a neighborhood, reducing as well to zero, the influence of values apart from this maximum.
Without considering the shape transformation due to the pooling layer, can we say that pooling is a kind of a dropout step?
Would the addition of a dropout (or DropConnect) layer, after a pooling layer, make sense in a CNN? And does it help the training process and generalization property?