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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 421–430 of 629 papers

TitleStatusHype
STOOD-X methodology: using statistical nonparametric test for OOD Detection Large-Scale datasets enhanced with explainability—0
Supervision Adaptation Balancing In-distribution Generalization and Out-of-distribution Detection—0
SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised Contrastive Learning and Euclidean Distance—0
Task Agnostic and Post-hoc Unseen Distribution Detection—0
Tensor-Train Point Cloud Compression and Efficient Approximate Nearest-Neighbor Search—0
The Compact Support Neural Network—0
The Conditional Entropy Bottleneck—0
The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches—0
Three Factors to Improve Out-of-Distribution Detection—0
TIME-LAPSE: Learning to say “I don't know” through spatio-temporal uncertainty scoring—0
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