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 251–260 of 629 papers

TitleStatusHype
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
Multi-Label Out-of-Distribution Detection with Spectral Normalized Joint EnergyCode0
Semi-supervised novelty detection using ensembles with regularized disagreementCode0
Hybrid Energy Based Model in the Feature Space for Out-of-Distribution DetectionCode0
Layer Adaptive Deep Neural Networks for Out-of-distribution DetectionCode0
Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution DetectionCode0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
kFolden: k-Fold Ensemble for Out-Of-Distribution DetectionCode0
Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generalization in OOD DetectionCode0
Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selections and ApproximationsCode0
Show:102550
← PrevPage 26 of 63Next →

No leaderboard results yet.