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 151–175 of 629 papers

TitleStatusHype
Learning Transferable Negative Prompts for Out-of-Distribution DetectionCode2
A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?Code0
Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt TuningCode0
Negative Label Guided OOD Detection with Pretrained Vision-Language ModelsCode1
BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection—0
Hyperbolic Metric Learning for Visual Outlier Detection—0
Out-of-Distribution Detection Using Peer-Class Generated by Large Language Model—0
Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)—0
Enhancing Out-of-Distribution Detection with Multitesting-based Layer-wise Feature Fusion—0
Energy Correction Model in the Feature Space for Out-of-Distribution Detection—0
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
Zero-shot Object-Level OOD Detection with Context-Aware Inpainting—0
Kernel PCA for Out-of-Distribution DetectionCode0
Learning with Mixture of Prototypes for Out-of-Distribution DetectionCode1
Towards Optimal Feature-Shaping Methods for Out-of-Distribution DetectionCode1
Comprehensive OOD Detection Improvements—0
NODI: Out-Of-Distribution Detection with Noise from Diffusion—0
Rethinking Test-time Likelihood: The Likelihood Path Principle and Its Application to OOD DetectionCode2
Show:102550
← PrevPage 7 of 26Next →

No leaderboard results yet.