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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 531–540 of 629 papers

TitleStatusHype
Zero-shot Object-Level OOD Detection with Context-Aware Inpainting—0
Full-Spectrum Out-of-Distribution Detection—0
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL—0
Exploring Vicinal Risk Minimization for Lightweight Out-of-Distribution Detection—0
VRA: Variational Rectified Activation for Out-of-distribution Detection—0
GDDA: Semantic OOD Detection on Graphs under Covariate Shift via Score-Based Diffusion Models—0
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation—0
Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection—0
Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data—0
General-Purpose Multi-Modal OOD Detection Framework—0
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