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 121–130 of 629 papers

TitleStatusHype
Reliability in Semantic Segmentation: Are We on the Right Track?Code1
Reliability in Semantic Segmentation: Can We Use Synthetic Data?Code1
Robust Out-of-distribution Detection for Neural NetworksCode1
ATOM: Robustifying Out-of-distribution Detection Using Outlier MiningCode1
Detection of out-of-distribution samples using binary neuron activation patternsCode1
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core QuantitiesCode1
Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized EmbeddingsCode1
Isotropy Maximization Loss and Entropic Score: Accurate, Fast, Efficient, Scalable, and Turnkey Neural Networks Out-of-Distribution Detection Based on The Principle of Maximum EntropyCode1
Energy-based Out-of-Distribution Detection for Graph Neural NetworksCode1
Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic FeaturesCode1
Show:102550
← PrevPage 13 of 63Next →

No leaderboard results yet.