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.

Paper

Janis Postels, Mattia Segu, Tao Sun, Luca Sieber, Luc Van Gool, Fisher Yu, Federico Tombari On the Practicality of Deterministic Epistemic Uncertainty ICML 2022

Citation

@inproceedings{postels2022practicality,
  title={On the Practicality of Deterministic Epistemic Uncertainty},
  author={Postels, Janis and Segu, Mattia and Sun, Tao and Van Gool, Luc and Yu, Fisher and Tombari, Federico},
  booktitle={International Conference on Machine Learning},
  year={2022}
}