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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 601–610 of 629 papers

TitleStatusHype
Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates—0
Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing—0
Bridging In- and Out-of-distribution Samples for Their Better Discriminability—0
The Compact Support Neural Network—0
The Conditional Entropy Bottleneck—0
kFolden: k-Fold Ensemble for Out-Of-Distribution Detection—0
KNN-Contrastive Learning for Out-of-Domain Intent Classification—0
Enhancing Out-of-Distribution Detection with Multitesting-based Layer-wise Feature Fusion—0
Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection—0
Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving—0
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