Model library is an effective tool for improving the performance of single-model Out-of-Distribution (OoD) detector, mainly through model selection and detector fusion. However, existing methods in the literature do not provide uncertainty quantification for model selection results. Additionally, the model ensemble process primarily focuses on controlling the True Positive Rate (TPR) while neglecting the False Positive Rate (FPR). In this paper, we emphasize the significance of the proportion of models in the library that identify the test sample as an OoD sample. This proportion holds crucial information and directly influences the error rate of OoD detection.To address this, we propose inverting the commonly-used sequential p-value strategies. We define the rejection region initially and then estimate the error rate. Furthermore, we introduce a novel perspective from change-point detection and propose an approach for proportion estimation with automatic hyperparameter selection. We name the proposed approach as DOS-Storey-based Detector Ensemble (DSDE). Experimental results on CIFAR10 and CIFAR100 demonstrate the effectiveness of our approach in tackling OoD detection challenges. Specifically, the CIFAR10 experiments show that DSDE reduces the FPR from 11.07% to 3.31% compared to the top-performing single-model detector.

翻译：模型库是提升单模型分布外检测器性能的有效工具，主要通过模型选择与检测器融合实现。然而，现有文献中的方法未能为模型选择结果提供不确定性量化。此外，模型集成过程主要关注控制真阳性率，却忽视了假阳性率。本文强调模型库中将测试样本识别为分布外样本的模型比例具有重要意义——该比例蕴含关键信息并直接影响分布外检测的错误率。为此，我们提出对常用的序列p值策略进行逆向运用：先定义拒绝域，再估计错误率。进一步，我们引入变化点检测的新视角，提出一种具有超参数自动选择能力的比例估计方法。我们将所提方法命名为基于DOS-Storey的检测器集成。在CIFAR10与CIFAR100数据集上的实验结果表明，该方法能有效应对分布外检测的挑战。具体而言，CIFAR10实验显示，相较于性能最优的单模型检测器，DSDE将假阳性率从11.07%降至3.31%。