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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 341–350 of 629 papers

TitleStatusHype
MOG: Molecular Out-of-distribution Generation with Energy-based Models—0
Multidimensional Uncertainty Quantification for Deep Neural Networks—0
Multi-layer Radial Basis Function Networks for Out-of-distribution Detection—0
Multiple Testing Framework for Out-of-Distribution Detection—0
Shifting Transformation Learning for Out-of-Distribution Detection—0
NADS: Neural Architecture Distribution Search for Uncertainty Awareness—0
Natural Attribute-based Shift Detection—0
Negative Sampling in Variational Autoencoders—0
Network Inversion for Uncertainty-Aware Out-of-Distribution Detection—0
Neural Network Out-of-Distribution Detection for Regression Tasks—0
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