Point of dropping weights in mini batch for purpose of regularization
Yes dropout is useful this case too. The purpose of dropout is to make the model generalize better by causing it to use all available weights from a given layer. It is supposed to make the subsequent layer not rely on a small number of activations from the previous layer, but to use them all.