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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 261–270 of 629 papers

TitleStatusHype
Semi-supervised novelty detection using ensembles with regularized disagreementCode0
Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generalization in OOD DetectionCode0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
Leveraging Perturbation Robustness to Enhance Out-of-Distribution DetectionCode0
Do Bayesian Variational Autoencoders Know What They Don't Know?Code0
Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution DetectionCode0
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and AccuracyCode0
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?Code0
Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented DialogCode0
Distribution Calibration for Out-of-Domain Detection with Bayesian ApproximationCode0
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