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:语义一致离群检测旨在通过访问未标记的额外数据集来区分意外的数据样本和意图中的数据分布。当不区分时,正常数据和意外数据共存会加剧模型的过拟合。为了解决这个问题,我们提出了一种新颖的不确定性感知最优传输方案。我们的方案包括一个能量传输机制,它评估不确定性变化的成本,以促进语义不可知表示的分配,以及一个增强不同聚类之间语义属性区分能力的聚类扩展策略,通过扩大相应聚类之间的边界距离来实现。此外,我们提出了一个T-能量分数,以缓解平行传输和分类器分支之间的幅度差距。在两个标准的语义一致离群检测基准测试上的广泛实验表明,上述超越同类方法27.69%和34.4%的FPR@95的OOD检测性能。