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arXiv · 2404.08476

Combining Statistical Depth and Fermat Distance for Uncertainty Quantification

Hai-Vy Nguyen·Fabrice Gamboa·Reda Chhaibi·Sixin Zhang·Serge Gratton·Thierry Giaccone
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Abstract

We measure the Out-of-domain uncertainty in the prediction of Neural Networks using a statistical notion called ``Lens Depth'' (LD) combined with Fermat Distance, which is able to capture precisely the ``depth'' of a point with respect to a distribution in feature space, without any assumption about the form of distribution. Our method has no trainable parameter. The method is applicable to any classification model as it is applied directly in feature space at test time and does not intervene in training process. As such, it does not impact the performance of the original model. The proposed method gives excellent qualitative result on toy datasets and can give competitive or better uncertainty estimation on standard deep learning datasets compared to strong baseline methods.

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BibTeXRIS

Hai-Vy Nguyen, Fabrice Gamboa, Reda Chhaibi, Sixin Zhang, Serge Gratton, Thierry Giaccone. 2024-04-12. Combining Statistical Depth and Fermat Distance for Uncertainty Quantification. https://arxiv.org/abs/2404.08476

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