Adaptive Covariance and Multi-Layer Alignment for Out-of-Distribution Detection

Out-of-distribution (OOD) detection plays a pivotal role in ensuring the reliability and trustworthiness of AI systems. Although existing approaches achieve strong performance by leveraging features, logits, or both, most of them cannot generalize well across domains due to either overlooking critical information in low-level representations from shallow layers or utilizing sub-optimal layer selection strategies. In this paper, we propose Multi-Layer Adaptive Mahalanobis-Cosine Similarity (ML-AMCoS), a method that adaptively integrates cosine similarity with class-conditional Gaussian distributions. ML-AMCoS is designed to capture subtle nuances in feature covariance when intra-class variance is low, while suppressing covariance noise when the variance is high. To optimize the utilization of each layer, we calculate contribution weights based on performance against pseudo-OOD samples, which are generated by cut-mixing in-distribution (ID) training images with the four corners of images from different classes. Extensive experiments across diverse domains demonstrate that our method significantly improves robustness and detection accuracy, achieving an average AUROC of 81.13\% and FPR@95 of 40.12\% on near-OOD detection, outperforming the state-of-the-art methods by 7.25\% and 7.08\%, respectively.