Regularization
Any technique that deliberately constrains a model to stop it memorising the training set.
Regularisation trades a little training accuracy for better generalisation. Common forms are penalising large weights, dropping random units during training, and stopping early. It is the main lever against overfitting when you cannot get more data.
In practice: Dropout randomly switches off units each step so the network cannot rely on any single one.