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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 101–125 of 629 papers

TitleStatusHype
Provable Guarantees for Understanding Out-of-distribution DetectionCode1
DICE: Leveraging Sparsification for Out-of-Distribution DetectionCode1
Trustworthy Long-Tailed ClassificationCode1
Generalized Out-of-Distribution Detection: A SurveyCode1
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution DetectionCode1
Well-classified Examples are Underestimated in Classification with Deep Neural NetworksCode1
Can multi-label classification networks know what they don't know?Code1
Types of Out-of-Distribution Texts and How to Detect ThemCode1
On the Impact of Spurious Correlation for Out-of-distribution DetectionCode1
Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIPCode1
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
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained EnvironmentsCode1
Exploring the Limits of Out-of-Distribution DetectionCode1
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary CodesCode1
Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive LearningCode1
Can multi-label classification networks know what they don’t know?Code1
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