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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
Enhancing Out-of-Distribution Detection with Extended Logit Normalization—0
Enhancing Out-of-Distribution Detection with Multitesting-based Layer-wise Feature Fusion—0
A Unified Approach Towards Active Learning and Out-of-Distribution Detection—0
Plugin estimators for selective classification with out-of-distribution detection—0
Enhancing Outlier Knowledge for Few-Shot Out-of-Distribution Detection with Extensible Local Prompts—0
Enhancing Near OOD Detection in Prompt Learning: Maximum Gains, Minimal Costs—0
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection—0
Enhancing Automated and Early Detection of Alzheimer's Disease Using Out-Of-Distribution Detection—0
A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic Segmentation—0
Energy Correction Model in the Feature Space for Out-of-Distribution Detection—0
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