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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 491–500 of 629 papers

TitleStatusHype
Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection—0
Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection—0
Contrastive Training for Improved Out-of-Distribution Detection—0
Why is the Mahalanobis Distance Effective for Anomaly Detection?—0
Contextualised Out-of-Distribution Detection using Pattern Identication—0
Unsupervised Evaluation of Out-of-distribution Detection: A Data-centric Perspective—0
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection—0
Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation—0
A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space—0
Computer Aided Diagnosis and Out-of-Distribution Detection in Glaucoma Screening Using Color Fundus Photography—0
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