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arXiv · 2208.01705

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

Mark Penrod·Harrison Termotto·Varshini Reddy·Jiayu Yao·Finale Doshi-Velez·Weiwei Pan
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Abstract

For responsible decision making in safety-critical settings, machine learning models must effectively detect and process edge-case data. Although existing works show that predictive uncertainty is useful for these tasks, it is not evident from literature which uncertainty-aware models are best suited for a given dataset. Thus, we compare six uncertainty-aware deep learning models on a set of edge-case tasks: robustness to adversarial attacks as well as out-of-distribution and adversarial detection. We find that the geometry of the data sub-manifold is an important factor in determining the success of various models. Our finding suggests an interesting direction in the study of uncertainty-aware deep learning models.

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Mark Penrod, Harrison Termotto, Varshini Reddy, Jiayu Yao, Finale Doshi-Velez, Weiwei Pan. 2022-08-02. Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry. https://arxiv.org/abs/2208.01705

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