Feature Density Estimation for Out-of-Distribution Detection via
  Normalizing Flows
Evan D. Cook Marc-Antoine Lavoie Steven L. Waslander
Out-of-distribution (OOD) detection is a critical task for safe deployment of
learning systems in the open world setting. In this work, we investigate the
use of feature density estimation via normalizing flows for OOD detection and
present a fully unsupervised approach which requires no exposure to OOD data,
avoiding researcher bias in OOD sample selection. This is a post-hoc method
which can be applied to any pretrained model, and involves training a
lightweight auxiliary normalizing flow model to perform the out-of-distribution
detection via density thresholding. Experiments on OOD detection in image
classification show strong results for far-OOD data detection with only a
single epoch of flow training, including 98.2% AUROC for ImageNet-1k vs.
Textures, which exceeds the state of the art by 7.8%. We additionally explore
the connection between the feature space distribution of the pretrained model
and the performance of our method. Finally, we provide insights into training
pitfalls that have plagued normalizing flows for use in OOD detection.