  Bringing deep neural networks (DNNs) into safety critical applications such
as automated driving, medical imaging and finance, requires a thorough
treatment of the model's uncertainties. Training deep neural networks is
already resource demanding and so is also their uncertainty quantification. In
this overview article, we survey methods that we developed to teach DNNs to be
uncertain when they encounter new object classes. Additionally, we present
training methods to learn from only a few labels with help of uncertainty
quantification. Note that this is typically paid with a massive overhead in
computation of an order of magnitude and more compared to ordinary network
training. Finally, we survey our work on neural architecture search which is
also an order of magnitude more resource demanding then ordinary network
training.
