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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 451–460 of 629 papers

TitleStatusHype
Back to the Basics: Revisiting Out-of-Distribution Detection BaselinesCode0
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
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection—0
WeShort: Out-of-distribution Detection With Weak Shortcut structure—0
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective—0
Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space—0
Multiple Testing Framework for Out-of-Distribution Detection—0
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