  Deep neural networks often suffer from overconfidence which can be partly
remedied by improved out-of-distribution detection. For this purpose, we
propose a novel approach that allows for the generation of out-of-distribution
datasets based on a given in-distribution dataset. This new dataset can then be
used to improve out-of-distribution detection for the given dataset and machine
learning task at hand. The samples in this dataset are with respect to the
feature space close to the in-distribution dataset and therefore realistic and
plausible. Hence, this dataset can also be used to safeguard neural networks,
i.e., to validate the generalization performance. Our approach first generates
suitable representations of an in-distribution dataset using an autoencoder and
then transforms them using our novel proposed Soft Brownian Offset method.
After transformation, the decoder part of the autoencoder allows for the
generation of these implicit out-of-distribution samples. This newly generated
dataset then allows for mixing with other datasets and thus improved training
of an out-of-distribution classifier, increasing its performance.
Experimentally, we show that our approach is promising for time series using
synthetic data. Using our new method, we also show in a quantitative case study
that we can improve the out-of-distribution detection for the MNIST dataset.
Finally, we provide another case study on the synthetic generation of
out-of-distribution trajectories, which can be used to validate trajectory
prediction algorithms for automated driving.
