  We compare the end-to-end training approach to a modular approach in which a
system is decomposed into semantically meaningful components. We focus on the
sample complexity aspect, in the regime where an extremely high accuracy is
necessary, as is the case in autonomous driving applications. We demonstrate
cases in which the number of training examples required by the end-to-end
approach is exponentially larger than the number of examples required by the
semantic abstraction approach.
