  Neural networks reach state-of-the-art performance in a variety of learning
tasks. However, a lack of understanding the decision making process yields to
an appearance as black box. We address this and propose ConstraintNet, a neural
network with the capability to constrain the output space in each forward pass
via an additional input. The prediction of ConstraintNet is proven within the
specified domain. This enables ConstraintNet to exclude unintended or even
hazardous outputs explicitly whereas the final prediction is still learned from
data. We focus on constraints in form of convex polytopes and show the
generalization to further classes of constraints. ConstraintNet can be
constructed easily by modifying existing neural network architectures. We
highlight that ConstraintNet is end-to-end trainable with no overhead in the
forward and backward pass. For illustration purposes, we model ConstraintNet by
modifying a CNN and construct constraints for facial landmark prediction tasks.
Furthermore, we demonstrate the application to a follow object controller for
vehicles as a safety-critical application. We submitted an approach and system
for the generation of safety-critical outputs of an entity based on
ConstraintNet at the German Patent and Trademark Office with the official
registration mark DE10 2019 119 739.
