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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 481–490 of 629 papers

TitleStatusHype
Unified Out-Of-Distribution Detection: A Model-Specific Perspective—0
A Simple Test-Time Method for Out-of-Distribution Detection—0
Who Needs Decoders? Efficient Estimation of Sequence-level Attributes—0
Revisiting flow generative models for Out-of-distribution detection—0
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors—0
ARES: Auxiliary Range Expansion for Outlier Synthesis—0
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection—0
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks—0
Revisiting Out-of-Distribution Detection: A Simple Baseline is Surprisingly Effective—0
An out-of-distribution discriminator based on Bayesian neural network epistemic uncertainty—0
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