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arXiv · 2605.20728

Early High-Frequency Injection for Geometry-Sensitive OOD Detection

Chuanjie Cheng·Ningkang Peng·Chenxi Liu·Yifan He·Peirong Ma·Yanhui Gu
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Abstract

Post-hoc OOD detectors score logits or features after training, so their success depends on the geometry already encoded in the representation. We revisit this assumption through a band-wise MMD^2 analysis across CE, SimCLR, SupCon, and the OOD-oriented representation method PALM. In our diagnostic, low-frequency input bands induce weaker ID/OOD feature discrepancy, whereas higher-frequency bands tend to provide stronger separability. This observation motivates EIHF, an input-side intervention that exposes high-frequency evidence before the first convolution without changing the training objective. EIHF is strongest for geometry-sensitive OOD detection: under matched training and scoring settings, it reshapes class-conditional feature geometry and reduces ID/OOD Mahalanobis score overlap. Experiments on CIFAR-100 and ImageNet-100 show gains on CIFAR-100 and the best average FPR95 with second-best average AUROC on ImageNet-100, while also revealing a limitation on the scene-centric Places shift. Code is available at https://anonymous.4open.science/r/EIHF.

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Chuanjie Cheng, Ningkang Peng, Chenxi Liu, Yifan He, Peirong Ma, Yanhui Gu. 2026-05-20. Early High-Frequency Injection for Geometry-Sensitive OOD Detection. https://arxiv.org/abs/2605.20728

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