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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 301–325 of 629 papers

TitleStatusHype
General-Purpose Multi-Modal OOD Detection Framework—0
GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model—0
GradPCA: Leveraging NTK Alignment for Reliable Out-of-Distribution Detection—0
Graph Synthetic Out-of-Distribution Exposure with Large Language Models—0
GRODIN: Improved Large-Scale Out-of-Domain detection via Back-propagation—0
GROOD: Gradient-Aware Out-of-Distribution Detection—0
Harnessing Large Language and Vision-Language Models for Robust Out-of-Distribution Detection—0
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection—0
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection—0
Holistic Sentence Embeddings for Better Out-of-Distribution Detection—0
HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar—0
How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?—0
How to Enable Uncertainty Estimation in Proximal Policy Optimization—0
How Useful are Gradients for OOD Detection Really?—0
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles—0
Hyperbolic Metric Learning for Visual Outlier Detection—0
Hypercone Assisted Contour Generation for Out-of-Distribution Detection—0
HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings—0
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection—0
Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss—0
Out-of-distribution detection for regression tasks: parameter versus predictor entropy—0
PAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection—0
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training—0
Improving Out-of-Distribution Detection with Markov Logic Networks—0
Improving Training and Inference of Face Recognition Models via Random Temperature Scaling—0
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