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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 391–400 of 629 papers

TitleStatusHype
Do Bayesian Variational Autoencoders Know What They Don't Know?Code0
Key Feature Replacement of In-Distribution Samples for Out-of-Distribution DetectionCode0
Out-of-Distribution Detection with Reconstruction Error and Typicality-based Penalty—0
Boosting Out-of-Distribution Detection with Multiple Pre-trained ModelsCode0
Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data—0
Solving Sample-Level Out-of-Distribution Detection on 3D Medical ImagesCode0
Improving Training and Inference of Face Recognition Models via Random Temperature Scaling—0
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective—0
Out-Of-Distribution Detection Is Not All You Need—0
TrustGAN: Training safe and trustworthy deep learning models through generative adversarial networksCode0
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