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Out of Distribution (OOD) Detection

Out of Distribution (OOD) Detection is the task of detecting instances that do not belong to the distribution the classifier has been trained on. OOD data is often referred to as "unseen" data, as the model has not encountered it during training.

OOD detection is typically performed by training a model to distinguish between in-distribution (ID) data, which the model has seen during training, and OOD data, which it has not seen. This can be done using a variety of techniques, such as training a separate OOD detector, or modifying the model's architecture or loss function to make it more sensitive to OOD data.

Papers

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Showing 561–570 of 629 papers

TitleStatusHype
How to Enable Uncertainty Estimation in Proximal Policy Optimization—0
How Useful are Gradients for OOD Detection Really?—0
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles—0
STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data—0
Hyperbolic Metric Learning for Visual Outlier Detection—0
Hypercone Assisted Contour Generation for Out-of-Distribution Detection—0
Estimating Soft Labels for Out-of-Domain Intent Detection—0
HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings—0
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection—0
STOOD-X methodology: using statistical nonparametric test for OOD Detection Large-Scale datasets enhanced with explainability—0
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