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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 271–280 of 629 papers

TitleStatusHype
Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space—0
Energy Correction Model in the Feature Space for Out-of-Distribution Detection—0
Energy-bounded Learning for Robust Models of Code—0
Comprehensive OOD Detection Improvements—0
Energy-based Out-of-distribution Detection for Multi-label Classification—0
A Baseline for Detecting Out-of-Distribution Examples in Image Captioning—0
Image Background Serves as Good Proxy for Out-of-distribution Data—0
Enclosing Prototypical Variational Autoencoder for Explainable Out-of-Distribution Detection—0
Efficient Out-of-Distribution Detection via CVAE data Generation—0
Computer Aided Diagnosis and Out-of-Distribution Detection in Glaucoma Screening Using Color Fundus Photography—0
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