As Deep Learning continues to yield successful applications in Computer Vision, the ability to quantify all forms of uncertainty is a paramount requirement for its safe and reliable deployment in the real-world. In this work, we leverage the formulation of variational inference in function space, where we associate Gaussian Processes (GPs) to both Bayesian CNN priors and variational family. Since GPs are fully determined by their mean and covariance functions, we are able to obtain predictive uncertainty estimates at the cost of a single forward pass through any chosen CNN architecture and for any supervised learning task. By leveraging the structure of the induced covariance matrices, we propose numerically efficient algorithms which enable fast training in the context of high-dimensional tasks such as depth estimation and semantic segmentation. Additionally, we provide sufficient conditions for constructing regression loss functions whose probabilistic counterparts are compatible with aleatoric uncertainty quantification.

本文提出一种基于变分推断的高维深度学习不确定性量化方法，使用高斯过程将贝叶斯卷积神经网络先验和变分族相结合，为任意有监督的学习任务提供了预测不确定性估计，同时提供了快速训练的方法。此外，该文还提供了构建回归损失函数的充分条件，使其概率对应物与aleatoric不确定性量化相容。

使用函数变分推断实现计算机视觉中的可扩展不确定性