Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture

• URL: http://arxiv.org/abs/2210.02676v1
• Date: Thu, 6 Oct 2022 04:47:51 GMT
• Title: Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture
• Authors: Myong Chol Jung, He Zhao, Joanna Dipnall, Belinda Gabbe, Lan Du
• Abstract summary: Uncertainty estimation is essential to make neural networks trustworthy in real-world applications. We propose a new multi-view classification framework for better uncertainty estimation and out-of-domain sample detection.
• Score: 5.868139834982011
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Uncertainty estimation is essential to make neural networks trustworthy in real-world applications. Extensive research efforts have been made to quantify and reduce predictive uncertainty. However, most existing works are designed for unimodal data, whereas multi-view uncertainty estimation has not been sufficiently investigated. Therefore, we propose a new multi-view classification framework for better uncertainty estimation and out-of-domain sample detection, where we associate each view with an uncertainty-aware classifier and combine the predictions of all the views in a principled way. The experimental results with real-world datasets demonstrate that our proposed approach is an accurate, reliable, and well-calibrated classifier, which predominantly outperforms the multi-view baselines tested in terms of expected calibration error, robustness to noise, and accuracy for the in-domain sample classification and the out-of-domain sample detection tasks.

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