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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 281–290 of 629 papers

TitleStatusHype
Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt TuningCode0
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
COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification—0
Out-of-distribution Partial Label Learning—0
Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution DetectionCode0
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