Combining Statistical Depth and Fermat Distance for Uncertainty Quantification

arXiv:2404.08476v1 Announce Type: cross
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 …

abstract arxiv cs.ai cs.lg distribution domain feature lens math.pr networks neural networks notion prediction quantification space stat.ap statistical stat.ml type uncertainty