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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 251–260 of 629 papers

TitleStatusHype
Entropic Issues in Likelihood-Based OOD Detection—0
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles—0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
Hyperbolic Metric Learning for Visual Outlier Detection—0
A Variational Information Theoretic Approach to Out-of-Distribution Detection—0
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU—0
Contextualised Out-of-Distribution Detection using Pattern Identication—0
A Metacognitive Approach to Out-of-Distribution Detection for Segmentation—0
KNN-Contrastive Learning for Out-of-Domain Intent Classification—0
Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection—0
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