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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 201–210 of 629 papers

TitleStatusHype
COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification—0
HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar—0
Curved Geometric Networks for Visual Anomaly Recognition—0
Falsehoods that ML researchers believe about OOD detection—0
FARE: A Deep Learning-Based Framework for Radar-based Face Recognition and Out-of-distribution Detection—0
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection—0
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation—0
Feature Purified Transformer With Cross-level Feature Guiding Decoder For Multi-class OOD and Anomaly Deteciton—0
Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection—0
Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning—0
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