  Deep Neural Networks for classification behave unpredictably when confronted
with inputs not stemming from the training distribution. This motivates
out-of-distribution detection (OOD) mechanisms. The usual lack of prior
information on out-of-distribution data renders the performance estimation of
detection approaches on unseen data difficult. Several contemporary evaluation
protocols are based on open set simulations, which average the performance over
up to five synthetic random splits of a dataset into in- and
out-of-distribution samples. However, the number of possible splits may be much
larger, and the performance of Deep Neural Networks is known to fluctuate
significantly depending on different sources of random variation. We
empirically demonstrate that current protocols may fail to provide reliable
estimates of the expected performance of OOD methods. By casting this
evaluation as a random process, we generalize the concept of open set
simulations and propose to estimate the performance of OOD methods using a
Monte Carlo approach that addresses the randomness.
