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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 221–230 of 629 papers

TitleStatusHype
Full-Spectrum Out-of-Distribution Detection—0
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL—0
Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning—0
Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data—0
Exploring Covariate and Concept Shift for Detection and Confidence Calibration of Out-of-Distribution Data—0
Contrastive Training for Improved Out-of-Distribution Detection—0
Exploiting Mixed Unlabeled Data for Detecting Samples of Seen and Unseen Out-of-Distribution Classes—0
Exploiting Diffusion Prior for Out-of-Distribution Detection—0
General-Purpose Multi-Modal OOD Detection Framework—0
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment—0
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