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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 261–270 of 629 papers

TitleStatusHype
Detecting Out-of-distribution Data through In-distribution Class PriorCode0
Multidimensional Uncertainty Quantification for Deep Neural Networks—0
Open-World Continual Learning: Unifying Novelty Detection and Continual Learning—0
Unified Out-Of-Distribution Detection: A Model-Specific Perspective—0
GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution DetectionCode1
Confidence-Aware and Self-Supervised Image Anomaly LocalisationCode0
AUTO: Adaptive Outlier Optimization for Test-Time OOD DetectionCode0
Reliability in Semantic Segmentation: Are We on the Right Track?Code1
Detecting Out-of-distribution Examples via Class-conditional Impressions Reappearing—0
MCROOD: Multi-Class Radar Out-Of-Distribution Detection—0
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