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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 291–300 of 629 papers

TitleStatusHype
Free Lunch for Generating Effective Outlier Supervision—0
Revisit PCA-based Technique for Out-of-Distribution DetectionCode0
AdaptiveMix: Improving GAN Training via Feature Space ShrinkageCode1
Detection of out-of-distribution samples using binary neuron activation patternsCode1
Do Bayesian Variational Autoencoders Know What They Don't Know?Code0
Key Feature Replacement of In-Distribution Samples for Out-of-Distribution DetectionCode0
Boosting Out-of-Distribution Detection with Multiple Pre-trained ModelsCode0
Out-of-Distribution Detection with Reconstruction Error and Typicality-based Penalty—0
Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data—0
Solving Sample-Level Out-of-Distribution Detection on 3D Medical ImagesCode0
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