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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 401–425 of 629 papers

TitleStatusHype
Reconstruction-based Out-of-Distribution Detection for Short-Range FMCW Radar—0
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective—0
Revisiting flow generative models for Out-of-distribution detection—0
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors—0
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection—0
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks—0
Revisiting Out-of-Distribution Detection: A Simple Baseline is Surprisingly Effective—0
Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection—0
Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation—0
Score Combining for Contrastive OOD Detection—0
SEE-OoD: Supervised Exploration For Enhanced Out-of-Distribution Detection—0
Semantic or Covariate? A Study on the Intractable Case of Out-of-Distribution Detection—0
Exploring Simple, High Quality Out-of-Distribution Detection with L2 Normalization—0
Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distribution Detection using Budding Ensemble Architecture for Object Detection—0
Sneakoscope: Revisiting Unsupervised Out-of-Distribution Detection—0
Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks—0
SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps—0
SR-OOD: Out-of-Distribution Detection via Sample Repairing—0
A statistical framework for efficient out of distribution detection in deep neural networks—0
STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data—0
STOOD-X methodology: using statistical nonparametric test for OOD Detection Large-Scale datasets enhanced with explainability—0
Supervision Adaptation Balancing In-distribution Generalization and Out-of-distribution Detection—0
SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised Contrastive Learning and Euclidean Distance—0
Task Agnostic and Post-hoc Unseen Distribution Detection—0
Tensor-Train Point Cloud Compression and Efficient Approximate Nearest-Neighbor Search—0
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