多视图数据的不确定性估计：看到全局的力量
Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture
摘要：
不确定性估计是使神经网络在现实世界中值得信赖的关键。已经进行了广泛的研究努力来量化和减少预测的不确定性。然而，大多数现有的工作是专为单峰数据，而多视图不确定性估计还没有得到充分的研究。因此，我们提出了一个新的多视图分类框架，更好的不确定性估计和域外样本检测，在那里我们将每个视图与一个不确定性感知分类器，并结合所有的意见，在一个原则性的方式预测。与真实世界的数据集的实验结果表明，我们提出的方法是一个准确，可靠，校准良好的分类器，主要优于多视图基线测试的预期校准误差，噪声的鲁棒性，以及准确性的域内样本分类和域外样本检测任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
