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 421–430 of 629 papers

TitleStatusHype
Out-Of-Distribution Detection In Unsupervised Continual Learning—0
Out-Of-Distribution Detection Is Not All You Need—0
UFO: Unidentified Foreground Object Detection in 3D Point Cloud—0
Out-of-Distribution Detection of Melanoma using Normalizing Flows—0
Uncertainty-Aware Multiple-Instance Learning for Reliable Classification: Application to Optical Coherence Tomography—0
Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)—0
Out-of-Distribution Detection Using Neural Rendering Generative Models—0
Out-of-Distribution Detection Using Peer-Class Generated by Large Language Model—0
Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation—0
Detecting Out-of-Distribution Examples with Gram Matrices—0
Show:102550
← PrevPage 43 of 63Next →

No leaderboard results yet.