Improving Normalizing Flows with the Approximate Mass for Out-of-Distribution Detection

Samy Chali, Inna Kucher, Marc Duranton, Jacques-Olivier Klein

Abstract: Normalizing flows are generative models that show poor performance on out-of-distribution (OOD) detection tasks with a likelihood-based test. In this study we focus on the "approximate mass" metric. We show that while it improves OOD detection performance, it has limitations under a maximum likelihood training. To solve this limitation we modify the training objective by incorporating the approximate mass. It smooths the learnt distribution in the vicinity of training in-distribution data. We measure an average of 97.6% AUROC in our experiments on different benchmarks, showing an improvement of 16% with respect to the best baseline we tested against.