How to build an overfitted network in order to increase performances
The reason of overfitting is generally because of the high(unnecessary) complexity model. So if you train a too complex(large) model, you will get a high training accuracy and low test set accuracy. Then you can start to fight with overfitting  with getting more data, dropout, regularization, early stopping, global average pooling, feature scale clipping or dropping some of layers from your network.