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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 61–70 of 629 papers

TitleStatusHype
Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric EnsemblesCode0
Out-of-Distribution Detection with Prototypical Outlier ProxyCode0
Distribution Shifts at Scale: Out-of-distribution Detection in Earth ObservationCode1
Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning—0
ITP: Instance-Aware Test Pruning for Out-of-Distribution DetectionCode0
Mining In-distribution Attributes in Outliers for Out-of-distribution DetectionCode0
Taylor Outlier ExposureCode0
EDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap Expansion—0
Out-of-Distribution Detection with Overlap Index—0
Revisiting Energy-Based Model for Out-of-Distribution DetectionCode0
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