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For responsible decision making in safety-critical settings, machine learningmodels must effectively detect and process edge-case data. Although existingworks show that predictive uncertainty is useful for these tasks, it is notevident from literature which uncertainty-aware models are best suited for agiven dataset. Thus, we compare six uncertainty-aware deep learning models on aset of edge-case tasks: robustness to adversarial attacks as well asout-of-distribution and adversarial detection. We find that the geometry of thedata sub-manifold is an important factor in determining the success of variousmodels. Our finding suggests an interesting direction in the study ofuncertainty-aware deep learning models.

Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry