Characterizing Submanifold Region for Out-of-Distribution Detection (Extended Abstract)

Detecting out-of-distribution (OOD) samples poses a significant safety challenge when deploying models in open-world scenarios. Advanced works assume that OOD and in-distributional (ID) samples exhibit a distribution discrepancy, showing an encouraging direction in estimating the uncertainty with embedding features or predicting outputs. In this work, we propose a data structure-aware approach to mitigate the sensitivity of distances to the 'curse of dimensionality', where high-dimensional features are mapped to the manifold of ID samples, leveraging the well-known manifold assumption. Specifically, we present a novel distance termed as tangent distance, which tackles the issue of generalizing the meaningfulness of distances on testing samples to detect OOD inputs. Extensive experiments show that the tangent distance performs competitively with other post hoc OOD detection baselines on common and large-scale benchmarks.