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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 341–350 of 629 papers

TitleStatusHype
Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing—0
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization—0
Three Factors to Improve Out-of-Distribution Detection—0
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images—0
HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar—0
General-Purpose Multi-Modal OOD Detection Framework—0
Large Class Separation is not what you need for Relational Reasoning-based OOD DetectionCode0
Random-Set Neural Networks (RS-NN)—0
Image Background Serves as Good Proxy for Out-of-distribution Data—0
Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation—0
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