  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.
