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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 541–550 of 629 papers

TitleStatusHype
DOI: Divergence-based Out-of-Distribution Indicators via Deep Generative Models—0
Are Bayesian neural networks intrinsically good at out-of-distribution detection?Code0
WiP Abstract : Robust Out-of-distribution Motion Detection and Localization in Autonomous CPS—0
Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time ApplicationsCode0
On the Importance of Regularisation & Auxiliary Information in OOD DetectionCode0
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models—0
Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation—0
Confidence-based Out-of-Distribution Detection: A Comparative Study and AnalysisCode0
On the Practicality of Deterministic Epistemic UncertaintyCode0
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU—0
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