PLSP（事前极限空间剖面分析）：超越检测的OOD预测——一种面向机器学习模型可靠性的预期性方法
PLSP (Pre-hoc Liminal Space Profiling): OOD Prediction over Detection -- An Anticipatory Approach for Machine Learning Model Reliability
- University of Wisconsin Madison（威斯康星大学麦迪逊分校）
- IMC University of Applied Sciences（IMC应用科学大学）
- Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出PLSP框架，将OOD检测转向事前预测，引入数据集无关的CREDS指标、可信度曲线和热图，以提升模型在分布偏移下的鲁棒性。
AI中文摘要：
分布外（OOD）数据对机器学习模型构成重大威胁，常常导致模型在部署期间失效。所有现有的OOD检测方法都是事后（post-hoc）的，依赖推理过程中的评估指标（如准确率和AUC-ROC）通过测量偏差来间接评估模型对OOD数据的响应。与现有方法不同，本工作将范式从OOD检测转向OOD预测，提出了一种名为PLSP的事前（pre-hoc）预期性框架用于OOD预测。我们做出了几项关键贡献：（a）提出了一种与数据集无关的指标，称为可信度评分（CREDS），用于OOD预测；（b）引入了可信度曲线来研究模型能够达到的最大可信度；（c）引入了可信度热图（以及表面下的体积）来刻画模型在不同数据集上的事前行为。这项工作为分布偏移下的信号处理提供了新的视角。跨多个数据集的实验表明，所提出的指标可作为提高机器学习模型在OOD预测方面鲁棒性的有价值的度量。
英文摘要：
Out-of-Distribution (OOD) data poses a significant threat to machine learning models, often leading to model failure during deployment. All existing OOD detection methods are post-hoc, relying on evaluation metrics such as accuracy and AUC-ROC during inference to indirectly assess the model's response to OOD data by measuring deviations. In contrast to existing approaches, the proposed work shifts the paradigm from OOD detection to OOD prediction by proposing a pre-hoc anticipatory framework called PLSP for OOD prediction. We make several key contributions: (a) a dataset-independent metric called the CREDibility Score (CREDS) is proposed for OOD prediction; (b) credibility curves are introduced to study the maximum credibility a model can attain; and (c) credibility heat maps (and volume under surface) are introduced to characterize pre-hoc model behavior across different datasets. This work provides a novel perspective on signal processing under distributional shifts. Experiments across multiple datasets demonstrate that the proposed metric serves as a valuable measure for improving the robustness of machine learning models toward OOD prediction.
