  Highly Autonomous Driving (HAD) systems rely on deep neural networks for the
visual perception of the driving environment. Such networks are trained on
large manually annotated databases. In this work, a semi-parametric approach to
one-shot learning is proposed, with the aim of bypassing the manual annotation
step required for training perceptions systems used in autonomous driving. The
proposed generative framework, coined Generative One-Shot Learning (GOL), takes
as input single one-shot objects, or generic patterns, and a small set of
so-called regularization samples used to drive the generative process. New
synthetic data is generated as Pareto optimal solutions from one-shot objects
using a set of generalization functions built into a generalization generator.
GOL has been evaluated on environment perception challenges encountered in
autonomous vision.
