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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 221–230 of 629 papers

TitleStatusHype
Improving Out-of-Distribution Detection in Echocardiographic View Classication through Enhancing Semantic Features—0
CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say NoCode1
SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised Contrastive Learning and Euclidean Distance—0
From Global to Local: Multi-scale Out-of-distribution DetectionCode0
How Good Are LLMs at Out-of-Distribution Detection?Code1
Out-of-distribution multi-view auto-encoders for prostate cancer lesion detection—0
Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing—0
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization—0
Three Factors to Improve Out-of-Distribution Detection—0
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images—0
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