arXiv AI
Jun 30
Exploiting Local Flatness for Efficient Out-of-Distribution Detection
arXiv:2606. 29952v1 Announce Type: cross Abstract: Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment.
Exploiting Local Flatness for Efficient Out-of-Distribution Detection
Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment. Among detection strategies, post-hoc methods are particularly attractive due to their efficiency, as they operate directly on pre-trained networks without requiring retraining.
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arXiv AI
Jul 15
Sparse Autoencoders for Interpretable Out-of-Distribution Detection
arXiv:2607. 12094v1 Announce Type: cross Abstract: Reliable detection of out-of-distribution (OOD) samples is crucial for the safe deployment of machine learning models.
arXiv Machine Learning
Sep 11
Optimizing Three Critical Factors for Practical and Effective OOD Detection Fine-Tuning
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 Machine Learning
Jun 9
phepy: Visual benchmarks and improvements for out-of-distribution detectors
arXiv:2503. 05169v2 Announce Type: replace Abstract: Applying machine learning to increasingly high-dimensional problems with sparse or biased training data increases the risk that a model is used on inputs outside its training domain.
arXiv AI
Jul 8
Medix: Out-of-Distribution Detection from Unlabeled Wild Data via Robust Gradient Statistics
arXiv:2510. 06505v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection plays a crucial role in ensuring the robustness of machine learning systems deployed in real-world applications.
arXiv Machine Learning
Jul 21
AOE: Exhaustive Out-of-Distribution Detection via Recalibrating Outlier Labels
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.
arXiv AI
Jun 2
CalArena: A Large-Scale Post-Hoc Calibration Benchmark
arXiv:2605. 30188v2 Announce Type: replace-cross Abstract: Reliable probability estimates are critical in many machine learning applications, yet modern classifiers are often poorly calibrated.
arXiv Machine Learning
Sep 14
PLSP (Pre-hoc Liminal Space Profiling): OOD Prediction over Detection -- An Anticipatory Approach for Machine Learning Model Reliability
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 Computer Vision
Sep 22
Combining Foundation Model Confidence and Monocular Depth for Training-Free Out-of-Distribution Segmentation
arXiv:2609.22896v1 Announce Type: new Abstract: Autonomous vehicles operating in open-world scenarios are inevitably confronted with previously unknown objects, such as exotic animals or loose cargo....
arXiv Machine Learning
Jun 9
Need We Teach Foundation Models What is a Generative Image? Gradient-Free Generative Artifact Detection via Analytic Spectral Adaptation
arXiv:2606. 07660v1 Announce Type: cross Abstract: Adapting foundation models to detect generative artifacts via gradient-based updates compromises their intrinsic representations.
arXiv Computer Vision
Sep 22
SAFe: Segment-guided Aggregation of Feature Densities for Anomaly-aware Segmentation
arXiv:2609.24204v1 Announce Type: new Abstract: Visual segmentation systems encounter objects outside their training distribution during real-world deployment, hindering reliable autonomous systems t...
arXiv Machine Learning
Jul 15
Beyond Perceptual Distance: Discrepancy Assessment on Deep Representation for Out-of-Distribution Detection with Diffusion Model
arXiv:2409. 10094v3 Announce Type: replace-cross Abstract: Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.