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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 581–590 of 629 papers

TitleStatusHype
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution DetectionCode0
Contextual Out-of-Domain Utterance Handling With Counterfeit Data AugmentationCode0
Out-of-distribution Detection Learning with Unreliable Out-of-distribution SourcesCode0
When and How Does In-Distribution Label Help Out-of-Distribution Detection?Code0
Out of Distribution Detection on ImageNet-OCode0
Are Bayesian neural networks intrinsically good at out-of-distribution detection?Code0
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood LearningCode0
Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution DetectionCode0
Identifying Incorrect Classifications with Balanced UncertaintyCode0
Hybrid Energy Based Model in the Feature Space for Out-of-Distribution DetectionCode0
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