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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 331–340 of 629 papers

TitleStatusHype
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty—0
MetaOOD: Automatic Selection of OOD Detection Models—0
Meta OOD Learning for Continuously Adaptive OOD Detection—0
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective—0
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images—0
WeiPer: OOD Detection using Weight Perturbations of Class Projections—0
Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss—0
Mitigating Hallucinations in YOLO-based Object Detection Models: A Revisit to Out-of-Distribution Detection—0
Mitigating the Modality Gap: Few-Shot Out-of-Distribution Detection with Multi-modal Prototypes and Image Bias Estimation—0
Mitral Regurgitation Recognition based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification—0
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