论文标题
一种实时不确定性估计的几何方法
A Geometric Method for Improved Uncertainty Estimation in Real-time
论文作者
论文摘要
机器学习分类器本质上是概率的，因此不可避免地涉及不确定性。预测特定输入正确的概率称为不确定性（或置信度）估计，对于风险管理至关重要。事后模型校准可以改善模型的不确定性估计，而无需重新培训，而无需更改模型。我们的工作为不确定性估计提出了一种基于几何的方法。粗略地说，我们使用现有训练输入的电流输入的几何距离作为估计不确定性的信号，然后使用标准的事后校准技术校准该信号（而不是模型的估计）。我们表明，通过广泛评估多个数据集和模型，我们的方法比最近提出的方法产生更好的不确定性估计。此外，我们还证明了在接近实时应用程序中执行方法的可能性。我们的代码可在我们的github https://github.com/nosleepdeveloper/geometric-calibrator上找到。
Machine learning classifiers are probabilistic in nature, and thus inevitably involve uncertainty. Predicting the probability of a specific input to be correct is called uncertainty (or confidence) estimation and is crucial for risk management. Post-hoc model calibrations can improve models' uncertainty estimations without the need for retraining, and without changing the model. Our work puts forward a geometric-based approach for uncertainty estimation. Roughly speaking, we use the geometric distance of the current input from the existing training inputs as a signal for estimating uncertainty and then calibrate that signal (instead of the model's estimation) using standard post-hoc calibration techniques. We show that our method yields better uncertainty estimations than recently proposed approaches by extensively evaluating multiple datasets and models. In addition, we also demonstrate the possibility of performing our approach in near real-time applications. Our code is available at our Github https://github.com/NoSleepDeveloper/Geometric-Calibrator.
