Epistemic Uncertainty Quantification For Pre-trained Neural Network

arXiv:2404.10124v1 Announce Type: new
Abstract: Epistemic uncertainty quantification (UQ) identifies where models lack knowledge. Traditional UQ methods, often based on Bayesian neural networks, are not suitable for pre-trained non-Bayesian models. Our study addresses quantifying epistemic uncertainty for any pre-trained model, which does not need the original training data or model modifications and can ensure broad applicability regardless of network architectures or training techniques. Specifically, we propose a gradient-based approach to assess epistemic uncertainty, analyzing the gradients of outputs relative to …

abstract arxiv bayesian cs.cv cs.lg data knowledge network networks neural network neural networks pre-trained model quantification study training training data type uncertainty