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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 151–160 of 629 papers

TitleStatusHype
Learning Transferable Negative Prompts for Out-of-Distribution DetectionCode2
A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?Code0
Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt TuningCode0
Negative Label Guided OOD Detection with Pretrained Vision-Language ModelsCode1
BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection—0
Hyperbolic Metric Learning for Visual Outlier Detection—0
Out-of-Distribution Detection Using Peer-Class Generated by Large Language Model—0
Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)—0
Enhancing Out-of-Distribution Detection with Multitesting-based Layer-wise Feature Fusion—0
Energy Correction Model in the Feature Space for Out-of-Distribution Detection—0
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