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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 276–300 of 629 papers

TitleStatusHype
Efficient Out-of-Distribution Detection via CVAE data Generation—0
Computer Aided Diagnosis and Out-of-Distribution Detection in Glaucoma Screening Using Color Fundus Photography—0
Model2Detector:Widening the Information Bottleneck for Out-of-Distribution Detection using a Handful of Gradient Steps—0
Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems—0
A Survey on Out-of-Distribution Detection in NLP—0
Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks—0
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation—0
Bayesian OOD detection with aleatoric uncertainty and outlier exposure—0
Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss—0
Effectiveness of Vision Language Models for Open-world Single Image Test Time Adaptation—0
CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue—0
EDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap Expansion—0
Improving Out-of-Distribution Detection in Echocardiographic View Classication through Enhancing Semantic Features—0
Cluster-aware Contrastive Learning for Unsupervised Out-of-distribution Detection—0
Mitral Regurgitation Recognition based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification—0
Model-free Test Time Adaptation for Out-Of-Distribution Detection—0
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering—0
EARLIN: Early Out-of-Distribution Detection for Resource-efficient Collaborative Inference—0
Dual Energy-Based Model with Open-World Uncertainty Estimation for Out-of-distribution Detection—0
Class-wise Thresholding for Robust Out-of-Distribution Detection—0
Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos—0
Dual-Adapter: Training-free Dual Adaptation for Few-shot Out-of-Distribution Detection—0
Classifier-head Informed Feature Masking and Prototype-based Logit Smoothing for Out-of-Distribution Detection—0
A Simple Test-Time Method for Out-of-Distribution Detection—0
Double Descent Meets Out-of-Distribution Detection: Theoretical Insights and Empirical Analysis on the role of model complexity—0
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