  Out-of-distribution (OOD) detection is indispensable for safely deploying
machine learning models in the wild. One of the key challenges is that models
lack supervision signals from unknown data, and as a result, can produce
overconfident predictions on OOD data. Recent work on outlier synthesis modeled
the feature space as parametric Gaussian distribution, a strong and restrictive
assumption that might not hold in reality. In this paper, we propose a novel
framework, Non-Parametric Outlier Synthesis (NPOS), which generates artificial
OOD training data and facilitates learning a reliable decision boundary between
ID and OOD data. Importantly, our proposed synthesis approach does not make any
distributional assumption on the ID embeddings, thereby offering strong
flexibility and generality. We show that our synthesis approach can be
mathematically interpreted as a rejection sampling framework. Extensive
experiments show that NPOS can achieve superior OOD detection performance,
outperforming the competitive rivals by a significant margin. Code is publicly
available at https://github.com/deeplearning-wisc/npos.
