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 281–290 of 629 papers

TitleStatusHype
Two-step counterfactual generation for OOD examples—0
Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection—0
Uncertainty-Aware Multiple-Instance Learning for Reliable Classification: Application to Optical Coherence Tomography—0
Energy-based Out-of-Distribution Detection for Graph Neural NetworksCode1
Cluster-aware Contrastive Learning for Unsupervised Out-of-distribution Detection—0
Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic FeaturesCode1
Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier DataCode1
Plugin estimators for selective classification with out-of-distribution detection—0
Interpretable Out-Of-Distribution Detection Using Pattern Identification—0
Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield EnergyCode0
Show:102550
← PrevPage 29 of 63Next →

No leaderboard results yet.