CORE: Robust Out-of-Distribution Detection via Confidence and Orthogonal Residual Scoring
#CORE #out-of-distribution detection #confidence scoring #orthogonal residual #robustness #machine learning #anomaly detection
📌 Key Takeaways
- CORE is a new method for detecting out-of-distribution data in machine learning models.
- It combines confidence scores with orthogonal residual scoring to improve detection robustness.
- The approach aims to enhance model reliability by identifying unfamiliar inputs more accurately.
- CORE addresses limitations in existing out-of-distribution detection techniques.
📖 Full Retelling
arXiv:2603.18290v1 Announce Type: new
Abstract: Out-of-distribution (OOD) detection is essential for deploying deep learning models reliably, yet no single method performs consistently across architectures and datasets -- a scorer that leads on one benchmark often falters on another. We attribute this inconsistency to a shared structural limitation: logit-based methods see only the classifier's confidence signal, while feature-based methods attempt to measure membership in the training distribu
🏷️ Themes
Machine Learning, Anomaly Detection
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Original Source
arXiv:2603.18290v1 Announce Type: new
Abstract: Out-of-distribution (OOD) detection is essential for deploying deep learning models reliably, yet no single method performs consistently across architectures and datasets -- a scorer that leads on one benchmark often falters on another. We attribute this inconsistency to a shared structural limitation: logit-based methods see only the classifier's confidence signal, while feature-based methods attempt to measure membership in the training distribu
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