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arXiv Machine Learning By Micha{\l} Kozyra, Gesine Reinert

TASTE: Task-Aware Out-of-Distribution Detection via Stein Operators

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TASTE (Task-Aware STEin operators) is a framework that links distribution shift to a model’s input sensitivity using Stein operators. It provides a geometric interpretation of shift as a projection onto the model’s sensitivity field, offers coordinate‑wise localisation, and delivers interpretable per‑pixel diagnostics for image data. Experiments on Gaussian shifts, MNIST, and CIFAR‑10/ImageNet‑1K benchmarks show that TASTE closely tracks task degradation and performs competitively with existing baselines, with gains varying by shift regime.

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