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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 291–300 of 629 papers

TitleStatusHype
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection—0
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune—0
Understanding Likelihood of Normalizing Flow and Image Complexity through the Lens of Out-of-Distribution Detection—0
Out-of-Distribution Detection and Data Drift Monitoring using Statistical Process Control—0
Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows—0
Kernel PCA for Out-of-Distribution DetectionCode0
Zero-shot Object-Level OOD Detection with Context-Aware Inpainting—0
Comprehensive OOD Detection Improvements—0
NODI: Out-Of-Distribution Detection with Noise from Diffusion—0
UFO: Unidentified Foreground Object Detection in 3D Point Cloud—0
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