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 441–450 of 629 papers

TitleStatusHype
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded CorruptionsCode0
Task Agnostic and Post-hoc Unseen Distribution Detection—0
Instance-Aware Observer Network for Out-of-Distribution Object Segmentation—0
A Simple Test-Time Method for Out-of-Distribution Detection—0
On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution DetectionCode0
Sample-dependent Adaptive Temperature Scaling for Improved CalibrationCode0
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors—0
A Baseline for Detecting Out-of-Distribution Examples in Image Captioning—0
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring ApproachCode0
Harnessing Out-Of-Distribution Examples via Augmenting Content and StyleCode0
Show:102550
← PrevPage 45 of 63Next →

No leaderboard results yet.