  Out-of-distribution detection seeks to identify novelties, samples that
deviate from the norm. The task has been found to be quite challenging,
particularly in the case where the normal data distribution consists of
multiple semantic classes (e.g., multiple object categories). To overcome this
challenge, current approaches require manual labeling of the normal images
provided during training. In this work, we tackle multi-class novelty detection
without class labels. Our simple but effective solution consists of two stages:
we first discover "pseudo-class" labels using unsupervised clustering. Then
using these pseudo-class labels, we are able to use standard supervised
out-of-distribution detection methods. We verify the performance of our method
by a favorable comparison to the state-of-the-art, and provide extensive
analysis and ablations.
