  To facilitate reliable deployments of autonomous robots in the real world,
Out-of-Distribution (OOD) detection capabilities are often required. A powerful
approach for OOD detection is based on density estimation with Normalizing
Flows (NFs). However, we find that prior work with NFs attempts to match the
complex target distribution topologically with naive base distributions leading
to adverse implications. In this work, we circumvent this topological mismatch
using an expressive class-conditional base distribution trained with an
information-theoretic objective to match the required topology. The proposed
method enjoys the merits of wide compatibility with existing learned models
without any performance degradation and minimum computation overhead while
enhancing OOD detection capabilities. We demonstrate superior results in
density estimation and 2D object detection benchmarks in comparison with
extensive baselines. Moreover, we showcase the applicability of the method with
a real-robot deployment.
