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 151–160 of 629 papers

TitleStatusHype
Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncodersCode1
Robust Out-of-distribution Detection for Neural NetworksCode1
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoderCode1
Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution DataCode1
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram MatricesCode1
Out-of-domain Detection for Natural Language Understanding in Dialog SystemsCode1
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
Likelihood Ratios for Out-of-Distribution DetectionCode1
Deep Anomaly Detection with Outlier ExposureCode1
ZClassifier: Temperature Tuning and Manifold Approximation via KL Divergence on Logit SpaceCode0
Show:102550
← PrevPage 16 of 63Next →

No leaderboard results yet.