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.

翻译：为了实现自主机器人在现实世界中的可靠部署，通常需要具备分布外（OOD）检测能力。一种基于归一化流（NFs）密度估计的OOD检测方法具有显著优势。然而，我们发现现有基于NFs的研究试图通过朴素基分布匹配复杂拓扑结构的目标分布，这会导致不良影响。本文通过使用信息论目标训练的表达性类条件基分布来匹配所需拓扑结构，从而规避了这种拓扑不匹配问题。所提方法具有与现有学习模型广泛兼容的优势，在提升OOD检测能力的同时不造成性能下降，且计算开销极小。我们在密度估计和二维目标检测基准测试中展示了优于多种基线方法的卓越结果。此外，我们还通过真实机器人部署验证了该方法的实用性。