TASTE: Task-Aware Out-of-Distribution Detection via Stein Operators
TL;DR
A task-aware framework based on so-called Stein operators, which links distribution shift to the input sensitivity of the model, with theoretical guarantees, and which aligns closely with task degradation while showing competitive performance compared to established baselines.
Abstract
Out-of-distribution detection methods are often either data-centric, detecting deviations from the training input distribution irrespective of their effect on a trained model, or model-centric, relying on classifier outputs without explicit reference to data geometry. We propose TASTE (Task-Aware STEin operators): a task-aware framework based on so-called Stein operators, which links distribution shift to the input sensitivity of the model. The resulting operators admit a geometric interpretation as a projection of distribution shift onto the sensitivity field of the model, with theoretical guarantees. Beyond detecting a shift, the construction enables its localisation through a coordinate-wise decomposition, and - for image data - provides interpretable per-pixel diagnostics. Experiments on controlled Gaussian shifts, MNIST under geometric perturbations, and CIFAR-10/ImageNet-1K perturbed benchmarks demonstrate that TASTE aligns closely with task degradation while showing competitive performance compared to established baselines, with gains that depend on the shift regime.
