Evidential Uncertainty Sets in Deep Classifiers Using Conformal Prediction
<|reference_start|>Evidential Uncertainty Sets in Deep Classifiers Using Conformal Prediction: In this paper, we propose Evidential Conformal Prediction (ECP) method for image classifiers to generate the conformal prediction sets. Our method is designed based on a non-conformity score function that has its roots in Evidential Deep Learning (EDL) as a method of quantifying model (epistemic) uncertainty in DNN classifiers. We use evidence that are derived from the logit values of target labels to compute the components of our non-conformity score function: the heuristic notion of uncertainty in CP, uncertainty surprisal, and expected utility. Our extensive experimental evaluation demonstrates that ECP outperforms three state-of-the-art methods for generating CP sets, in terms of their set sizes and adaptivity while maintaining the coverage of true labels.<|reference_end|>
@article{karimi2024evidential,
    title={Evidential Uncertainty Sets in Deep Classifiers Using Conformal
  Prediction},
    author={Hamed Karimi and Reza Samavi},
    journal={arXiv preprint arXiv:2406.10787},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10787},
    primaryClass={cs.LG cs.AI cs.CV stat.ML}
}