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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 211–220 of 629 papers

TitleStatusHype
FindMeIfYouCan: Bringing Open Set metrics to near , far and farther Out-of-Distribution Object Detection—0
Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection—0
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification—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
Free Lunch for Generating Effective Outlier Supervision—0
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
Contrastive Training for Improved Out-of-Distribution Detection—0
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