Enhancing Out-of-Distribution Detection Under Covariate Shifts: A Full-Spectrum Contrastive Denoising Framework

Out-of-distribution (OOD) detection is crucial for identifying samples that deviate from the training distribution, thereby enhancing the reliability of deep neural network models. However, existing OOD detection methods primarily address semantic shifts, where an image’s inherent semantics have changed, and often overlook covariate shifts, which are prevalent in real-world scenarios. For instance, variations in image contrast, lighting, or viewpoints can alter input features while keeping the semantic content intact. To address this, we propose the Full-Spectrum Contrastive Denoising (FSCD) framework, which improves OOD detection under covariate shifts. FSCD first establishes a robust semantic boundary and then refines feature representations through fine-tuning. Specifically, FSCD employs a dual-level perturbation augmentation module to simulate covariate shifts and a feature contrastive denoising module to effectively distinguish in-distribution samples from OOD samples. Extensive experiments on three benchmarks demonstrate that FSCD achieves state-of-the-art performance, with AUROC improvements of up to 0.51% on DIGITS, 0.55% on OBJECTS, and 2.09% on COVID compared to the previous best method while also maintaining the highest classification accuracy on covariate-shifted in-distribution samples.