  Out-of-distribution (OoD) detection for data-based programs is a goal of
paramount importance. Common approaches in the literature tend to train
detectors requiring inside-of-distribution (in-distribution, or IoD) and OoD
validation samples, and/or implement confidence metrics that are often abstract
and therefore difficult to interpret. In this work, we propose to use existing
work from the field of explainable AI, namely the PARTICUL pattern
identification algorithm, in order to build more interpretable and robust OoD
detectors for visual classifiers. Crucially, this approach does not require to
retrain the classifier and is tuned directly to the IoD dataset, making it
applicable to domains where OoD does not have a clear definition. Moreover,
pattern identification allows us to provide images from the IoD dataset as
reference points to better explain the confidence scores. We demonstrates that
the detection capabilities of this approach are on par with existing methods
through an extensive benchmark across four datasets and two definitions of OoD.
In particular, we introduce a new benchmark based on perturbations of the IoD
dataset which provides a known and quantifiable evaluation of the discrepancy
between the IoD and OoD datasets that serves as a reference value for the
comparison between various OoD detection methods. Our experiments show that the
robustness of all metrics under test does not solely depend on the nature of
the IoD dataset or the OoD definition, but also on the architecture of the
classifier, which stresses the need for thorough experimentations for future
work on OoD detection.
