Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks
<|reference_start|>Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks: Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks, including abstained prediction, out-of-distribution detection, and aleatoric uncertainty quantification. The latest goal is disentanglement: the construction of multiple estimators that are each tailored to one and only one task. Hence, there is a plethora of recent advances with different intentions - that often entirely deviate from practical behavior. This paper conducts a comprehensive evaluation of numerous uncertainty estimators across diverse tasks on ImageNet. We find that, despite promising theoretical endeavors, disentanglement is not yet achieved in practice. Additionally, we reveal which uncertainty estimators excel at which specific tasks, providing insights for practitioners and guiding future research toward task-centric and disentangled uncertainty estimation methods. Our code is available at https://github.com/bmucsanyi/bud.<|reference_end|>
@article{mucsányi2024benchmarking,
    title={Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for
  Specialized Tasks},
    author={B'alint Mucs'anyi and Michael Kirchhof and Seong Joon Oh},
    journal={arXiv preprint arXiv:2402.19460},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.19460},
    primaryClass={cs.LG stat.ML}
}