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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 111–120 of 629 papers

TitleStatusHype
On the Out-of-distribution Generalization of Probabilistic Image ModellingCode1
Semantically Coherent Out-of-Distribution DetectionCode1
Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic SegmentationCode1
OODformer: Out-Of-Distribution Detection TransformerCode1
Uncertainty-Aware Reliable Text ClassificationCode1
On Out-of-distribution Detection with Energy-based ModelsCode1
Out-of-Distribution Detection Using Union of 1-Dimensional SubspacesCode1
A Simple Fix to Mahalanobis Distance for Improving Near-OOD DetectionCode1
InFlow: Robust outlier detection utilizing Normalizing FlowsCode1
Provably Robust Detection of Out-of-distribution Data (almost) for freeCode1
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