仅用正常数据训练，就能识别异常输入，避免模型盲目自信。
Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes
- 用高斯过程量化神经网络输出的不确定性
- 无需异常数据即可构建有效检测边界
- 适合无异常样本可用的现实场景
深度神经网络通常基于封闭世界假设构建，难以泛化到分布外（OOD）数据，导致其对错误预测过度自信，可能在安全关键应用中引发灾难性后果。现有方法多依赖训练阶段的异常数据进行模型训练或超参数调优，但现实中异常样本常不可得，限制了检测性能。为此，本文提出一种基于高斯过程的OOD检测方法，仅利用正常数据建立决策边界。核心思想是通过多类别高斯过程对DNN的未约束软标签输出进行不确定性量化，并基于高斯过程后验预测分布的差异，定义得分函数以区分正常与潜在异常数据。在常规图像分类数据集和真实世界图像数据集上的两个案例研究显示，当训练阶段无异常样本时，该方法优于现有最优的OOD检测方法。
原文摘要 · Abstract (English)
Deep neural networks (DNNs) are often constructed under the closed-world assumption, which may fail to generalize to the out-of-distribution (OOD) data. This leads to DNNs producing overconfident wrong predictions and can result in disastrous consequences in safety-critical applications. Existing OOD detection methods mainly rely on curating a set of OOD data for model training or hyper-parameter tuning to distinguish OOD data from training data (also known as in-distribution data or InD data). However, OOD samples are not always available during the training phase in real-world applications, hindering the OOD detection accuracy. To overcome this limitation, we propose a Gaussian-process-based OOD detection method to establish a decision boundary based on InD data only. The basic idea is to perform uncertainty quantification of the unconstrained softmax scores of a DNN via a multi-class Gaussian process (GP), and then define a score function to separate InD and potential OOD data based on their fundamental differences in the posterior predictive distribution from the GP. Two case studies on conventional image classification datasets and real-world image datasets are conducted to demonstrate that the proposed method outperforms the state-of-the-art OOD detection methods when OOD samples are not observed in the training phase.
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