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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 201–210 of 629 papers

TitleStatusHype
Score Combining for Contrastive OOD Detection—0
Hypercone Assisted Contour Generation for Out-of-Distribution Detection—0
A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection—0
FARE: A Deep Learning-Based Framework for Radar-based Face Recognition and Out-of-distribution Detection—0
DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detection—0
ARES: Auxiliary Range Expansion for Outlier Synthesis—0
Harnessing Large Language and Vision-Language Models for Robust Out-of-Distribution Detection—0
Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks—0
Multi-layer Radial Basis Function Networks for Out-of-distribution Detection—0
Overcoming Shortcut Problem in VLM for Robust Out-of-Distribution Detection—0
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