  Out-of-distribution (OOD) detection is indispensable for machine learning
models deployed in the open world. Recently, the use of an auxiliary outlier
dataset during training (also known as outlier exposure) has shown promising
performance. As the sample space for potential OOD data can be prohibitively
large, sampling informative outliers is essential. In this work, we propose a
novel posterior sampling-based outlier mining framework, POEM, which
facilitates efficient use of outlier data and promotes learning a compact
decision boundary between ID and OOD data for improved detection. We show that
POEM establishes state-of-the-art performance on common benchmarks. Compared to
the current best method that uses a greedy sampling strategy, POEM improves the
relative performance by 42.0% and 24.2% (FPR95) on CIFAR-10 and CIFAR-100,
respectively. We further provide theoretical insights on the effectiveness of
POEM for OOD detection.
