  Several areas have been improved with Deep Learning during the past years.
For non-safety related products adoption of AI and ML is not an issue, whereas
in safety critical applications, robustness of such approaches is still an
issue. A common challenge for Deep Neural Networks (DNN) occur when exposed to
out-of-distribution samples that are previously unseen, where DNNs can yield
high confidence predictions despite no prior knowledge of the input.
  In this paper we analyse two supervisors on two well-known DNNs with varied
setups of training and find that the outlier detection performance improves
with the quality of the training procedure. We analyse the performance of the
supervisor after each epoch during the training cycle, to investigate
supervisor performance as the accuracy converges. Understanding the
relationship between training results and supervisor performance is valuable to
improve robustness of the model and indicates where more work has to be done to
create generalized models for safety critical applications.
