论文标题
回归先验网络
Regression Prior Networks
论文作者
论文摘要
先前的网络是最近开发的模型类别，产生可解释的不确定性度量，并且已被证明在一系列任务上都超过了最先进的合奏方法。它们还可以通过集合分配蒸馏（End $^2 $）来我们精确地蒸馏出模型集合，从而将其精度，校准和不确定性估计保留在单个模型中。但是，到目前为止，先前的网络仅用于分类任务。这项工作将先前的网络扩展到结束$^2 $，通过考虑普通宽度分布来回归任务。回归先验网络的属性在综合数据，选定的UCI数据集和单眼深度估计任务上进行了证明，在该任务中，它们可以通过合奏方法产生性能竞争。
Prior Networks are a recently developed class of models which yield interpretable measures of uncertainty and have been shown to outperform state-of-the-art ensemble approaches on a range of tasks. They can also be used to distill an ensemble of models via Ensemble Distribution Distillation (EnD$^2$), such that its accuracy, calibration and uncertainty estimates are retained within a single model. However, Prior Networks have so far been developed only for classification tasks. This work extends Prior Networks and EnD$^2$ to regression tasks by considering the Normal-Wishart distribution. The properties of Regression Prior Networks are demonstrated on synthetic data, selected UCI datasets and a monocular depth estimation task, where they yield performance competitive with ensemble approaches.
