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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 531–540 of 629 papers

TitleStatusHype
Exploring Covariate and Concept Shift for Detection and Confidence Calibration of Out-of-Distribution Data—0
DOODLER: Determining Out-Of-Distribution Likelihood from Encoder Reconstructions—0
Entropic Issues in Likelihood-Based OOD Detection—0
No True State-of-the-Art? OOD Detection Methods are Inconsistent across DatasetsCode0
kFolden: k-Fold Ensemble for Out-Of-Distribution DetectionCode0
NoiER: An Approach for Training more Reliable Fine-TunedDownstream Task Models—0
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain DetectionCode0
Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems—0
Revealing the Distributional Vulnerability of Discriminators by Implicit GeneratorsCode0
CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue—0
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