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 41–50 of 629 papers

TitleStatusHype
A Theoretical Study on Solving Continual LearningCode1
Augmenting Softmax Information for Selective Classification with Out-of-Distribution DataCode1
A Hybrid Architecture for Out of Domain Intent Detection and Intent DiscoveryCode1
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution DataCode1
Detection of out-of-distribution samples using binary neuron activation patternsCode1
Background Data Resampling for Outlier-Aware ClassificationCode1
Deep Anomaly Detection with Outlier ExposureCode1
Balanced Energy Regularization Loss for Out-of-distribution DetectionCode1
Accuracy on In-Domain Samples Matters When Building Out-of-Domain detectors: A Reply to Marek et al. (2021)Code1
A Benchmark and Evaluation for Real-World Out-of-Distribution Detection Using Vision-Language ModelsCode1
Show:102550
← PrevPage 5 of 63Next →

No leaderboard results yet.