用凸化网络的自助法，高效准确地估算CNN预测不确定性。
Uncertainty Quantification in CNN Through the Bootstrap of Convex Neural Networks
- 基于凸化神经网络构建自助法框架，保证不确定性估计的理论一致性。
- 计算开销远低于同类方法，利用热启动避免每次重新训练模型。
- 可推广至任意神经网络，适合医疗等需可靠置信度的场景。
尽管卷积神经网络（CNN）广受欢迎，其不确定性量化（UQ）问题却长期被忽视。缺乏高效的UQ工具严重限制了CNN在医学等对预测不确定性至关重要的领域的应用。现有少数深度学习UQ方法中，均未具备能保证不确定性质量的理论一致性。为此，本文提出一种基于自助法的新框架，用于预测不确定性的估计。该方法通过凸化神经网络实现自助法的理论一致性。与同类方法相比，本方法计算负载显著更低，因其在每次自助采样时利用热启动，无需从头重新拟合模型。此外，我们还提出一种新型迁移学习方法，使该框架可适用于任意神经网络。实验表明，该方法在多个图像数据集上性能显著优于其他基准CNN及当前最先进的方法。
原文摘要 · Abstract (English)
Despite the popularity of Convolutional Neural Networks (CNN), the problem of uncertainty quantification (UQ) of CNN has been largely overlooked. Lack of efficient UQ tools severely limits the application of CNN in certain areas, such as medicine, where prediction uncertainty is critically important. Among the few existing UQ approaches that have been proposed for deep learning, none of them has theoretical consistency that can guarantee the uncertainty quality. To address this issue, we propose a novel bootstrap based framework for the estimation of prediction uncertainty. The inference procedure we use relies on convexified neural networks to establish the theoretical consistency of bootstrap. Our approach has a significantly less computational load than its competitors, as it relies on warm-starts at each bootstrap that avoids refitting the model from scratch. We further explore a novel transfer learning method so our framework can work on arbitrary neural networks. We experimentally demonstrate our approach has a much better performance compared to other baseline CNNs and state-of-the-art methods on various image datasets.
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