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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 411–420 of 629 papers

TitleStatusHype
Out-of-Distribution Detection for LiDAR-based 3D Object Detection—0
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune—0
TTA-OOD: Test-time Augmentation for Improving Out-of-Distribution Detection in Gastrointestinal Vision—0
Out of distribution detection for skin and malaria images—0
Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem—0
Two-step counterfactual generation for OOD examples—0
Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning—0
Out-of-distribution Detection in Medical Image Analysis: A survey—0
Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE—0
BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection—0
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