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 281–290 of 629 papers

TitleStatusHype
Enclosing Prototypical Variational Autoencoder for Explainable Out-of-Distribution Detection—0
Efficient Out-of-Distribution Detection via CVAE data Generation—0
Computer Aided Diagnosis and Out-of-Distribution Detection in Glaucoma Screening Using Color Fundus Photography—0
Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space—0
Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems—0
A Survey on Out-of-Distribution Detection in NLP—0
Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks—0
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation—0
Bayesian OOD detection with aleatoric uncertainty and outlier exposure—0
Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss—0
Show:102550
← PrevPage 29 of 63Next →

No leaderboard results yet.