  The ability of deep learning models to generalize well across different
scenarios depends primarily on the quality and quantity of annotated data.
Labeling large amounts of data for all possible scenarios that a model may
encounter would not be feasible; if even possible. We propose a framework to
deal with limited labeled training data and demonstrate it on the application
of vision-based vehicle control. We show how limited steering angle data
available for only one condition can be transferred to multiple different
weather scenarios. This is done by leveraging unlabeled images in a
teacher-student learning paradigm complemented with an image-to-image
translation network. The translation network transfers the images to a new
domain, whereas the teacher provides soft supervised targets to train the
student on this domain. Furthermore, we demonstrate how utilization of
auxiliary networks can reduce the size of a model at inference time, without
affecting the accuracy. The experiments show that our approach generalizes well
across multiple different weather conditions using only ground truth labels
from one domain.
