  We analyze new generalization bounds for deep learning models trained by
transfer learning from a source to a target task. Our bounds utilize a quantity
called the majority predictor accuracy, which can be computed efficiently from
data. We show that our theory is useful in practice since it implies that the
majority predictor accuracy can be used as a transferability measure, a fact
that is also validated by our experiments.
