Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry
This work addresses the problem of selecting effective uncertainty-aware models for safety-critical applications, but it is incremental as it builds on existing comparisons without introducing new methods.
The study compared six uncertainty-aware deep learning models on edge-case tasks like adversarial attacks and out-of-distribution detection, finding that data sub-manifold geometry significantly influences model success.
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.