Whereas the ability of deep networks to produce useful predictions has been amply demonstrated, estimating the reliability of these predictions remains challenging. Sampling approaches such as MC-Dropout and Deep Ensembles have emerged as the most popular ones for this purpose. Unfortunately, they require many forward passes at inference time, which slows them down. Sampling-free approaches can be faster but suffer from other drawbacks, such as lower reliability of uncertainty estimates, difficulty of use, and limited applicability to different types of tasks and data. In this work, we introduce a sampling-free approach that is generic and easy to deploy, while producing reliable uncertainty estimates on par with state-of-the-art methods at a significantly lower computational cost. It is predicated on training the network to produce the same output with and without additional information about it. At inference time, when no prior information is given, we use the network's own prediction as the additional information. We then take the distance between the predictions with and without prior information as our uncertainty measure. We demonstrate our approach on several classification and regression tasks. We show that it delivers results on par with those of Ensembles but at a much lower computational cost.

翻译：尽管深度网络生成有效预测的能力已得到充分证明，但评估这些预测的可靠性仍具挑战性。MC-Dropout和深度集成等采样方法已成为最主流的可靠性评估手段，但它们在推理时需要多次前向传播，导致计算速度缓慢。无采样方法虽然更快，却存在其他缺陷，例如不确定性估计可靠性较低、难以使用、以及对不同任务和数据类型的适用性有限。本研究提出一种兼具通用性与易用性的无采样方法，在显著降低计算成本的同时，其不确定性估计的可靠性可与最先进方法媲美。该方法的核心思想是：训练网络在有无辅助信息的情况下输出相同结果。推理时，在无先验信息条件下，我们将网络自身的预测作为辅助信息，并将有无先验信息时的预测结果之间的距离作为不确定性度量。我们在多个分类与回归任务中验证了该方法，结果表明其性能与集成方法相当，但计算成本大幅降低。