Predictive variability due to data ambiguities has typically been addressed via construction of dedicated models with built-in probabilistic capabilities that are trained to predict uncertainty estimates as variables of interest. These approaches require distinct architectural components and training mechanisms, may include restrictive assumptions and exhibit overconfidence, i.e., high confidence in imprecise predictions. In this work, we propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity. The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions. It is architecture agnostic and can be applied to any feed-forward deterministic network without changes to the architecture or training procedure. Experiments on regression tasks on imaging and non-imaging input data show the method's ability to generate diverse and multi-modal predictive distributions, and a desirable correlation of the estimated uncertainty with the prediction error.

翻译：数据歧义导致的预测变异性通常通过构建具有内置概率能力的专用模型来解决，这些模型经过训练可将不确定性估计作为感兴趣变量进行预测。这些方法需要不同的架构组件和训练机制，可能包含限制性假设并表现出过度自信，即对不精确预测持有高置信度。本文提出一种用于估计预测不确定性（考虑数据歧义）的后验采样策略。该方法可为给定输入生成多种合理输出，且不假设预测分布具有参数形式。它不依赖特定架构，可应用于任何前馈确定性网络，无需修改架构或训练流程。在基于图像和非图像输入数据的回归任务实验表明，该方法具有生成多样化和多模态预测分布的能力，且估计不确定性与预测误差之间存在理想的关联性。