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 161–170 of 629 papers

TitleStatusHype
Out-of-distribution Partial Label Learning—0
COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification—0
Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution DetectionCode0
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection—0
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune—0
Understanding Likelihood of Normalizing Flow and Image Complexity through the Lens of Out-of-Distribution Detection—0
Out-of-Distribution Detection and Data Drift Monitoring using Statistical Process Control—0
Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows—0
Kernel PCA for Out-of-Distribution DetectionCode0
Learning with Mixture of Prototypes for Out-of-Distribution DetectionCode1
Show:102550
← PrevPage 17 of 63Next →

No leaderboard results yet.