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 571–580 of 629 papers

TitleStatusHype
TagOOD: A Novel Approach to Out-of-Distribution Detection via Vision-Language Representations and Class Center LearningCode0
Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion ImagesCode0
Out-of-Distribution Detection for Medical Applications: Guidelines for Practical EvaluationCode0
Contrastive Learning for OOD in Object detectionCode0
What If the Input is Expanded in OOD Detection?Code0
Out-of-distribution Detection in Classifiers via GenerationCode0
Task-Driven Detection of Distribution Shifts with Statistical Guarantees for Robot LearningCode0
Taylor Outlier ExposureCode0
Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generalization in OOD DetectionCode0
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring ApproachCode0
Show:102550
← PrevPage 58 of 63Next →

No leaderboard results yet.