用单模型密度估计实现高效不确定性量化与分布外检测
A Simple and Effective Method for Uncertainty Quantification and OOD Detection
- 基于核密度估计构建特征空间密度场
- 在2D合成数据和CIFAR-10/SVHN任务中优于基线
- 无需贝叶斯或集成，适合资源受限场景
贝叶斯神经网络和深度集成方法虽能进行不确定性量化，但计算开销大且存储需求高。本文提出一种基于特征空间密度的单确定性模型方法，用于分布偏移和分布外（OOD）检测。具体地，利用核密度估计生成的信息势场近似训练集的特征空间密度，并通过比较测试样本的特征表示与该密度，有效判断是否发生分布偏移。在2D合成数据集（Two Moons 和 Three Spirals）以及 CIFAR-10 与 SVHN 的 OOD 检测任务上进行了实验，结果表明该方法优于基线模型。
原文摘要 · Abstract (English)
Bayesian neural networks and deep ensemble methods have been proposed for uncertainty quantification; however, they are computationally intensive and require large storage. By utilizing a single deterministic model, we can solve the above issue. We propose an effective method based on feature space density to quantify uncertainty for distributional shifts and out-of-distribution (OOD) detection. Specifically, we leverage the information potential field derived from kernel density estimation to approximate the feature space density of the training set. By comparing this density with the feature space representation of test samples, we can effectively determine whether a distributional shift has occurred. Experiments were conducted on a 2D synthetic dataset (Two Moons and Three Spirals) as well as an OOD detection task (CIFAR-10 vs. SVHN). The results demonstrate that our method outperforms baseline models.
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