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May, 2024
基于反事实梯度的神经网络预测可信度量化
Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks
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Mohit Prabhushankar, Ghassan AlRegib
TL;DR
利用反事实梯度的方差作为信任度量衡,提出了GradTrust来探测大规模神经网络的误判率,并在ImageNet和Kinetics-400数据集上进行验证,结果表明GradTrust在37个实验模式中表现得最好。
Abstract
The widespread adoption of
deep neural networks
in machine learning calls for an objective quantification of esoteric trust. In this paper we propose
gradtrust
, a
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