Energy-Based Open-World Uncertainty Modeling for Confidence Calibration

Confidence calibration is of great importance to the reliability of decisions made by machine learning systems. However, discriminative classifiers based on deep neural networks are often criticized for producing overconfident predictions that fail to reflect the true correctness likelihood of class...

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Bibliographic Details
Published in:arXiv.org 2021-08
Main Authors: Wang, Yezhen, Li, Bo, Tong Che, Zhou, Kaiyang, Liu, Ziwei, Li, Dongsheng
Format: Article
Language:English
Subjects:
Artificial neural networks
Calibration
Classification
Entropy (Information theory)
Machine learning
Modelling
Uncertainty
Online Access:Get full text
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Summary:Confidence calibration is of great importance to the reliability of decisions made by machine learning systems. However, discriminative classifiers based on deep neural networks are often criticized for producing overconfident predictions that fail to reflect the true correctness likelihood of classification accuracy. We argue that such an inability to model uncertainty is mainly caused by the closed-world nature in softmax: a model trained by the cross-entropy loss will be forced to classify input into one of \(K\) pre-defined categories with high probability. To address this problem, we for the first time propose a novel \(K\)+1-way softmax formulation, which incorporates the modeling of open-world uncertainty as the extra dimension. To unify the learning of the original \(K\)-way classification task and the extra dimension that models uncertainty, we propose a novel energy-based objective function, and moreover, theoretically prove that optimizing such an objective essentially forces the extra dimension to capture the marginal data distribution. Extensive experiments show that our approach, Energy-based Open-World Softmax (EOW-Softmax), is superior to existing state-of-the-art methods in improving confidence calibration.
ISSN:2331-8422