SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 291–300 of 629 papers

TitleStatusHype
Improvements on Uncertainty Quantification for Node Classification via Distance-Based RegularizationCode0
Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selections and ApproximationsCode0
Key Feature Replacement of In-Distribution Samples for Out-of-Distribution DetectionCode0
kFolden: k-Fold Ensemble for Out-Of-Distribution DetectionCode0
Semi-supervised novelty detection using ensembles with regularized disagreementCode0
Adversarial Self-Supervised Learning for Out-of-Domain DetectionCode0
Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution DetectionCode0
ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection AlgorithmsCode0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
Igeood: An Information Geometry Approach to Out-of-Distribution DetectionCode0
Show:102550
← PrevPage 30 of 63Next →

No leaderboard results yet.