  A key feature of out-of-distribution (OOD) detection is to exploit a trained
neural network by extracting statistical patterns and relationships through the
multi-layer classifier to detect shifts in the expected input data
distribution. Despite achieving solid results, several state-of-the-art methods
rely on the penultimate or last layer outputs only, leaving behind valuable
information for OOD detection. Methods that explore the multiple layers either
require a special architecture or a supervised objective to do so. This work
adopts an original approach based on a functional view of the network that
exploits the sample's trajectories through the various layers and their
statistical dependencies. It goes beyond multivariate features aggregation and
introduces a baseline rooted in functional anomaly detection. In this new
framework, OOD detection translates into detecting samples whose trajectories
differ from the typical behavior characterized by the training set. We validate
our method and empirically demonstrate its effectiveness in OOD detection
compared to strong state-of-the-art baselines on computer vision benchmarks.
