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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 381–390 of 629 papers

TitleStatusHype
Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation—0
Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning—0
On the Learnability of Out-of-distribution Detection—0
DIsoN: Decentralized Isolation Networks for Out-of-Distribution Detection in Medical Imaging—0
Beyond Mahalanobis-Based Scores for Textual OOD Detection—0
Benchmarking Post-Hoc Unknown-Category Detection in Food Recognition—0
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel—0
OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples—0
OOD Aware Supervised Contrastive Learning—0
Discriminability-Driven Channel Selection for Out-of-Distribution Detection—0
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