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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 371–380 of 629 papers

TitleStatusHype
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring ApproachCode0
Out of Distribution Detection via Neural Network AnchoringCode1
Harnessing Out-Of-Distribution Examples via Augmenting Content and StyleCode0
Back to the Basics: Revisiting Out-of-Distribution Detection BaselinesCode0
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed RecognitionCode1
Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent ThresholdCode0
Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion ImagesCode0
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering—0
SHELS: Exclusive Feature Sets for Novelty Detection and Continual Learning Without Class BoundariesCode0
POEM: Out-of-Distribution Detection with Posterior SamplingCode1
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