Domain adaptation is learning a discriminative classifier or other predictor in the presence of a shift of data distribution between the source/training domain and the target/test domain [GUA+16].

Deep neural networks (DNNs) employ a large number of parameters to learn complex dependencies of outputs on inputs, but overfitting often occurs as a result.

Large DNNs are also slow to converge.

The dropout method implements the intuitive idea of randomly dropping units (along with their connections) from a network during training [SHK+14].