Dual Feature Decoupling for Fine-Grained OOD Detection

• URL: http://arxiv.org/abs/2606.05536v1
• Date: Thu, 04 Jun 2026 00:38:21 GMT
• Title: Dual Feature Decoupling for Fine-Grained OOD Detection
• Abstract summary: We propose a novel Dual Feature Decoupling Network (DFDNet) to address fine-grained OOD detection.<n>The DFDNet comprises two key components: a spatial-frequency decoupling module and a reconstruction-guided decoupling module.<n>Our method achieves competitive performance improvements on multiple datasets.
• Score: 21.94764693480303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Out-of-distribution detection (OOD) is an indispensable technique when applying machine learning models to real-world scenarios. Most existing OOD detection methods have been developed under the idealized assumption of large inter-class distributional differences, while largely overlooking fine-grained tasks characterized by subtle variations, such as medical image classification and vehicle recognition. The high visual similarity among fine-grained subcategories, together with the interference of background factors, makes OOD detection extremely challenging. To tackle this problem, we propose a novel Dual Feature Decoupling Network (DFDNet), which addresses fine-grained OOD detection from the perspective of feature disentanglement. The proposed DFDNet comprises two key components: a spatial-frequency decoupling module and a reconstruction-guided decoupling module. The spatial-frequency decoupling module is designed to preserve content features that are discriminative for classification while suppressing task-irrelevant style information. On the other hand, the reconstruction-guided decoupling module introduces a novel pixel-level adversarial reconstruction task to further remove low-level, non-discriminative information and enhance category-specific high-level semantic representations. Extensive experiments demonstrate that our method achieves competitive performance improvements on multiple datasets.

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