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A key feature of out-of-distribution (OOD) detection is to exploit a trainedneural network by extracting statistical patterns and relationships through themulti-layer classifier to detect shifts in the expected input datadistribution. Despite achieving solid results, several state-of-the-art methodsrely on the penultimate or last layer outputs only, leaving behind valuableinformation for OOD detection. Methods that explore the multiple layers eitherrequire a special architecture or a supervised objective to do so. This workadopts an original approach based on a functional view of the network thatexploits the sample's trajectories through the various layers and theirstatistical dependencies. It goes beyond multivariate features aggregation andintroduces a baseline rooted in functional anomaly detection. In this newframework, OOD detection translates into detecting samples whose trajectoriesdiffer from the typical behavior characterized by the training set. We validateour method and empirically demonstrate its effectiveness in OOD detectioncompared to strong state-of-the-art baselines on computer vision benchmarks.

A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection