  Deep learning models excel when the data distribution during training aligns
with testing data. Yet, their performance diminishes when faced with
out-of-distribution (OOD) samples, leading to great interest in the field of
OOD detection. Current approaches typically assume that OOD samples originate
from an unconcentrated distribution complementary to the training distribution.
While this assumption is appropriate in the traditional unsupervised OOD
(U-OOD) setting, it proves inadequate when considering the place of deployment
of the underlying deep learning model. To better reflect this real-world
scenario, we introduce the novel setting of continual U-OOD detection. To
tackle this new setting, we propose a method that starts from a U-OOD detector,
which is agnostic to the OOD distribution, and slowly updates during deployment
to account for the actual OOD distribution. Our method uses a new U-OOD scoring
function that combines the Mahalanobis distance with a nearest-neighbor
approach. Furthermore, we design a confidence-scaled few-shot OOD detector that
outperforms previous methods. We show our method greatly improves upon strong
baselines from related fields.
