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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 181–190 of 629 papers

TitleStatusHype
Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling RepresentationsCode0
STOOD-X methodology: using statistical nonparametric test for OOD Detection Large-Scale datasets enhanced with explainability—0
Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning—0
Few-Shot Graph Out-of-Distribution Detection with LLMs—0
VisTa: Visual-contextual and Text-augmented Zero-shot Object-level OOD Detection—0
ProHOC: Probabilistic Hierarchical Out-of-Distribution Classification via Multi-Depth NetworksCode0
Benchmarking Post-Hoc Unknown-Category Detection in Food Recognition—0
Leveraging Perturbation Robustness to Enhance Out-of-Distribution DetectionCode0
AdaSCALE: Adaptive Scaling for OOD DetectionCode0
Mitigating Hallucinations in YOLO-based Object Detection Models: A Revisit to Out-of-Distribution Detection—0
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