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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

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Showing 251–300 of 629 papers

TitleStatusHype
TTA-OOD: Test-time Augmentation for Improving Out-of-Distribution Detection in Gastrointestinal Vision—0
Improving Out-of-Distribution Detection by Combining Existing Post-hoc MethodsCode0
Enhancing OOD Detection Using Latent DiffusionCode0
SeTAR: Out-of-Distribution Detection with Selective Low-Rank ApproximationCode0
Exploiting Diffusion Prior for Out-of-Distribution Detection—0
Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites ParadoxCode0
FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar—0
Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distribution Detection using Budding Ensemble Architecture for Object Detection—0
Towards Out-of-Distribution Detection in Vocoder Recognition via Latent Feature Reconstruction—0
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS—0
Effectiveness of Vision Language Models for Open-world Single Image Test Time Adaptation—0
When and How Does In-Distribution Label Help Out-of-Distribution Detection?Code0
WeiPer: OOD Detection using Weight Perturbations of Class Projections—0
Concept Matching with Agent for Out-of-Distribution DetectionCode0
Enhancing Near OOD Detection in Prompt Learning: Maximum Gains, Minimal Costs—0
Dual-Adapter: Training-free Dual Adaptation for Few-shot Out-of-Distribution Detection—0
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification—0
A Unified Approach Towards Active Learning and Out-of-Distribution Detection—0
Multi-Label Out-of-Distribution Detection with Spectral Normalized Joint EnergyCode0
Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layerCode0
Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving—0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
Feature Purified Transformer With Cross-level Feature Guiding Decoder For Multi-class OOD and Anomaly Deteciton—0
Out-of-distribution Detection in Medical Image Analysis: A survey—0
Gradient-Regularized Out-of-Distribution DetectionCode0
Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and DetectionCode0
VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution DetectionCode0
On the Learnability of Out-of-distribution Detection—0
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector—0
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
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
COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification—0
Out-of-distribution Partial Label Learning—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
Kernel PCA for Out-of-Distribution DetectionCode0
Zero-shot Object-Level OOD Detection with Context-Aware Inpainting—0
Comprehensive OOD Detection Improvements—0
NODI: Out-Of-Distribution Detection with Noise from Diffusion—0
UFO: Unidentified Foreground Object Detection in 3D Point Cloud—0
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