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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 401–410 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
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