论文标题
使用单独的配方与虚拟残差的单独配方进行质量不确定性估算
The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals
论文作者
论文摘要
我们为回归问题中的不确定性估计提出了一个新的优化框架。现有方法可以量化目标估计中的误差，但它们倾向于低估该错误。为了获得观察中固有的预测不确定性，我们提出了一种新的可分离公式，以估算信号及其不确定性，避免过度拟合的效果。通过将目标估计和不确定性估计分解，我们还控制信号估计和不确定性估计之间的平衡。我们进行了三种类型的实验：带有模拟数据，年龄估计和深度估计的回归。我们证明，所提出的方法的表现优于信号和不确定性估计的最新技术。
We propose a new optimization framework for aleatoric uncertainty estimation in regression problems. Existing methods can quantify the error in the target estimation, but they tend to underestimate it. To obtain the predictive uncertainty inherent in an observation, we propose a new separable formulation for the estimation of a signal and of its uncertainty, avoiding the effect of overfitting. By decoupling target estimation and uncertainty estimation, we also control the balance between signal estimation and uncertainty estimation. We conduct three types of experiments: regression with simulation data, age estimation, and depth estimation. We demonstrate that the proposed method outperforms a state-of-the-art technique for signal and uncertainty estimation.
