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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 261–270 of 629 papers

TitleStatusHype
Effectiveness of Vision Language Models for Open-world Single Image Test Time Adaptation—0
When and How Does In-Distribution Label Help Out-of-Distribution Detection?Code0
WeiPer: OOD Detection using Weight Perturbations of Class Projections—0
Concept Matching with Agent for Out-of-Distribution DetectionCode0
Enhancing Near OOD Detection in Prompt Learning: Maximum Gains, Minimal Costs—0
Dual-Adapter: Training-free Dual Adaptation for Few-shot Out-of-Distribution Detection—0
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification—0
A Unified Approach Towards Active Learning and Out-of-Distribution Detection—0
Multi-Label Out-of-Distribution Detection with Spectral Normalized Joint EnergyCode0
Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving—0
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