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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 441–450 of 629 papers

TitleStatusHype
Out-of-distribution Partial Label Learning—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
Two-step counterfactual generation for OOD examples—0
UFO: Unidentified Foreground Object Detection in 3D Point Cloud—0
Uncertainty-Aware Multiple-Instance Learning for Reliable Classification: Application to Optical Coherence Tomography—0
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking—0
Uncertainty-Estimation with Normalized Logits for Out-of-Distribution Detection—0
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models—0
Understanding Likelihood of Normalizing Flow and Image Complexity through the Lens of Out-of-Distribution Detection—0
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