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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 241–250 of 629 papers

TitleStatusHype
EPA: Neural Collapse Inspired Robust Out-of-Distribution Detector—0
Interpretable Out-Of-Distribution Detection Using Pattern Identification—0
Entropic Issues in Likelihood-Based OOD Detection—0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection—0
Holistic Sentence Embeddings for Better Out-of-Distribution Detection—0
HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar—0
How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?—0
A Variational Information Theoretic Approach to Out-of-Distribution Detection—0
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU—0
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