Semantically coherent out-of-distribution (SCOOD) detection aims to discern outliers from the intended data distribution with access to unlabeled extra set. The coexistence of in-distribution and out-of-distribution samples will exacerbate the model overfitting when no distinction is made. To address this problem, we propose a novel uncertainty-aware optimal transport scheme. Our scheme consists of an energy-based transport (ET) mechanism that estimates the fluctuating cost of uncertainty to promote the assignment of semantic-agnostic representation, and an inter-cluster extension strategy that enhances the discrimination of semantic property among different clusters by widening the corresponding margin distance. Furthermore, a T-energy score is presented to mitigate the magnitude gap between the parallel transport and classifier branches. Extensive experiments on two standard SCOOD benchmarks demonstrate the above-par OOD detection performance, outperforming the state-of-the-art methods by a margin of 27.69% and 34.4% on FPR@95, respectively.

翻译：语义一致分布外检测（SCOOD）旨在利用未标注的外部数据集，从目标数据分布中识别离群样本。当不加以区分时，分布内与分布外样本的共存将加剧模型过拟合问题。为应对这一挑战，我们提出了一种新颖的不确定性感知最优传输方案。该方案包含两项核心机制：一是基于能量的传输（ET）机制，通过估算不确定性的动态成本以促进语义无关表征的分配；二是簇间扩展策略，通过放大不同簇间的边界距离来增强语义属性的判别能力。此外，我们构建了T能量评分以缓解并行传输与分类器分支间的幅度差异。在两个标准SCOOD基准上的大量实验表明，该方法在FPR@95指标上分别以27.69%和34.4%的优势超越现有最先进方法，展现出卓越的分布外检测性能。