  The ability to detect unfamiliar or unexpected images is essential for safe
deployment of computer vision systems. In the context of classification, the
task of detecting images outside of a model's training domain is known as
out-of-distribution (OOD) detection. While there has been a growing research
interest in developing post-hoc OOD detection methods, there has been
comparably little discussion around how these methods perform when the
underlying classifier is not trained on a clean, carefully curated dataset. In
this work, we take a closer look at 20 state-of-the-art OOD detection methods
in the (more realistic) scenario where the labels used to train the underlying
classifier are unreliable (e.g. crowd-sourced or web-scraped labels). Extensive
experiments across different datasets, noise types & levels, architectures and
checkpointing strategies provide insights into the effect of class label noise
on OOD detection, and show that poor separation between incorrectly classified
ID samples vs. OOD samples is an overlooked yet important limitation of
existing methods. Code: https://github.com/glhr/ood-labelnoise
