  When applying deep learning models in open-world scenarios, active learning
(AL) strategies are crucial for identifying label candidates from a nearly
infinite amount of unlabeled data. In this context, robust out-of-distribution
(OOD) detection mechanisms are essential for handling data outside the target
distribution of the application. However, current works investigate both
problems separately. In this work, we introduce SISOM as the first unified
solution for both AL and OOD detection. By leveraging feature space distance
metrics SISOM combines the strengths of the currently independent tasks to
solve both effectively. We conduct extensive experiments showing the problems
arising when migrating between both tasks. In these evaluations SISOM
underlined its effectiveness by achieving first place in two of the widely used
OpenOOD benchmarks and second place in the remaining one. In AL, SISOM
outperforms others and delivers top-1 performance in three benchmarks
