arXiv:2603. 07571v3 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is critical in safety-sensitive applications.
arXiv:2609.01027v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection predicts whether a test image belongs to none of the predefined classes. To evaluate this task, benchmarks need ima...
The paper introduces a framework for out-of-distribution (OOD) detection that addresses the trade‑off between detection performance and classification accuracy caused by fine‑tuning with auxiliary outlier data. It optimizes three factors—model reminder, data sampling, and representation learning—by proposing Self‑Knowledge Distillation to preserve accuracy, Semi‑hard Outlier Sampling to enhance detection with minimal data, and Outlier‑aware Supervised Contrastive Learning to improve ID‑OOD separability. The combined approach yields cumulative gains, outperforming existing methods on diverse benchmarks, especially in long‑tailed scenarios, and offers a robust baseline for real‑world OOD detection.
arXiv:2606. 17352v1 Announce Type: new Abstract: We introduce MM++ (Multilayer Mahalanobis++), a fully unsupervised, strictly post-hoc, and scale-invariant framework for Out-of-Distribution (OOD) detection.
MM++ (Multilayer Mahalanobis++) is a post‑hoc, scale‑invariant framework for out‑of‑distribution detection that builds a joint feature space by selecting discriminative intermediate layers based on entropy density drops and fusing them with the final representation. It uses a Ledoit‑Wolf regularized tied covariance matrix to stabilize the space, allowing reliable distance estimation without requiring auxiliary OOD data, classifier fine‑tuning, or architectural changes. The method achieves robust performance across different architectures for both near‑ and far‑OOD scenarios.
arXiv:2607. 19393v1 Announce Type: cross Abstract: While auditing a perturbation-based OOD detector on a document benchmark, we recorded an AUROC of 0.
arXiv:2606. 01973v1 Announce Type: new Abstract: Open-set test-time adaptation (TTA) updates models on new data in the presence of input shifts and unknown output classes.
The paper introduces PLSP (Pre-hoc Liminal Space Profiling), an anticipatory framework for predicting out-of-distribution (OOD) data before inference. It proposes a dataset‑independent metric called the CREDibility Score (CREDS) and introduces credibility curves and heat maps to analyze a model’s maximum credibility and behavior across datasets. Experiments on multiple datasets show that CREDS can improve model robustness to OOD prediction.
arXiv:2605. 28021v2 Announce Type: replace Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs may deviate from the training distribution and overconfident predictions on unknown samples can lead to unreliable decisions.
The paper investigates zero‑shot out‑of‑distribution (OOD) detection in medical imaging using vision‑language models (VLMs). It shows that intermediate layers, rather than only the final layer, provide valuable OOD signals and that the best layer depends on the imaging modality. To overcome instability in entropy‑based layer selection, the authors introduce a multi‑resolution entropy estimation that aggregates histogram statistics across scales, achieving consistent improvements over state‑of‑the‑art methods on the MIDOG and OASIS benchmarks.
arXiv:2607. 16283v1 Announce Type: cross Abstract: The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before.
arXiv:2508.10148v2 Announce Type: replace-cross Abstract: Accurate and explainable out-of-distribution (OOD) detection is required to use machine learning systems safely. Previous work has shown that...
arXiv:2606. 29952v1 Announce Type: cross Abstract: Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment.