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On the Practicality of Deterministic Epistemic Uncertainty

Janis Postels^*, Mattia Segu^*, Tao Sun, Luca Daniel Sieber, Luc Van Gool, Fisher Yu, Federico Tombari

* Equal contribution

Proceedings of the International Conference on Machine Learning (ICML) 2022

Abstract

A set of novel approaches for estimating epistemic uncertainty in deep neural networks with a single forward pass has recently emerged as a valid alternative to Bayesian Neural Networks. On the premise of informative representations, these deterministic uncertainty methods (DUMs) achieve strong performance on detecting out-of-distribution (OOD) data while adding negligible computational costs at inference time. However, it remains unclear whether DUMs are well calibrated and can seamlessly scale to real-world applications - both prerequisites for their practical deployment. To this end, we first provide a taxonomy of DUMs, and evaluate their calibration under continuous distributional shifts. Then, we extend them to semantic segmentation. We find that, while DUMs scale to realistic vision tasks and perform well on OOD detection, the practicality of current methods is undermined by poor calibration under distributional shifts.

BibTeX

@inproceedings{postels2022practicality,
  title={On the Practicality of Deterministic Epistemic Uncertainty},
  author={Postels, Janis and Segu, Mattia and Sun, Tao and Sieber, Luca Daniel and Van Gool, Luc and Yu, Fisher and Tombari, Federico},
  booktitle={International Conference on Machine Learning},
  year={2022},
  volume={162},
  pages={17870--17909},
  url={https://proceedings.mlr.press/v162/postels22a.html}
}