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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 151–200 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
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
Learning with Mixture of Prototypes for Out-of-Distribution DetectionCode1
Kernel PCA for Out-of-Distribution DetectionCode0
Zero-shot Object-Level OOD Detection with Context-Aware Inpainting—0
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
UFO: Unidentified Foreground Object Detection in 3D Point Cloud—0
MOODv2: Masked Image Modeling for Out-of-Distribution DetectionCode2
EPA: Neural Collapse Inspired Robust Out-of-Distribution Detector—0
Discriminability-Driven Channel Selection for Out-of-Distribution Detection—0
Test-Time Linear Out-of-Distribution DetectionCode1
Towards Reliable AI Model Deployments: Multiple Input Mixup for Out-of-Distribution DetectionCode0
HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings—0
GROOD: Gradient-Aware Out-of-Distribution Detection—0
Identity Curvature Laplace Approximation for Improved Out-of-Distribution DetectionCode0
Fast Decision Boundary based Out-of-Distribution DetectorCode0
Reliability in Semantic Segmentation: Can We Use Synthetic Data?Code1
EAT: Towards Long-Tailed Out-of-Distribution DetectionCode1
PAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection—0
Model-free Test Time Adaptation for Out-Of-Distribution Detection—0
ID-like Prompt Learning for Few-Shot Out-of-Distribution DetectionCode1
RankFeat&RankWeight: Rank-1 Feature/Weight Removal for Out-of-distribution DetectionCode0
Towards Few-shot Out-of-Distribution Detection—0
Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data—0
Distilling the Unknown to Unveil CertaintyCode0
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning—0
Improvements on Uncertainty Quantification for Node Classification via Distance-Based RegularizationCode0
Out-of-distribution Detection Learning with Unreliable Out-of-distribution SourcesCode0
Detecting Out-of-Distribution Through the Lens of Neural CollapseCode0
Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos—0
Classifier-head Informed Feature Masking and Prototype-based Logit Smoothing for Out-of-Distribution Detection—0
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