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 561–570 of 629 papers

TitleStatusHype
XOOD: Extreme Value Based Out-Of-Distribution Detection For Image ClassificationCode0
Analysis of Confident-Classifiers for Out-of-distribution DetectionCode0
Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution DetectionCode0
Structural Entropy Guided Unsupervised Graph Out-Of-Distribution DetectionCode0
Adversarial Self-Supervised Learning for Out-of-Domain DetectionCode0
A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution DetectionCode0
Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layerCode0
Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield EnergyCode0
Out-of-Distribution Detection by Leveraging Between-Layer Transformation SmoothnessCode0
T2FNorm: Extremely Simple Scaled Train-time Feature Normalization for OOD DetectionCode0
Show:102550
← PrevPage 57 of 63Next →

No leaderboard results yet.