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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 581–590 of 629 papers

TitleStatusHype
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS—0
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training—0
Improving Out-of-Distribution Detection with Markov Logic Networks—0
Improving Training and Inference of Face Recognition Models via Random Temperature Scaling—0
Task Agnostic and Post-hoc Unseen Distribution Detection—0
Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks—0
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU—0
Informative Outlier Matters: Robustifying Out-of-distribution Detection Using Outlier Mining—0
Enhancing Out-of-Distribution Detection with Extended Logit Normalization—0
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