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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 341–350 of 629 papers

TitleStatusHype
Model2Detector:Widening the Information Bottleneck for Out-of-Distribution Detection using a Handful of Gradient Steps—0
Model-free Test Time Adaptation for Out-Of-Distribution Detection—0
Effectiveness of Vision Language Models for Open-world Single Image Test Time Adaptation—0
MOG: Molecular Out-of-distribution Generation with Energy-based Models—0
EDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap Expansion—0
Improving Out-of-Distribution Detection in Echocardiographic View Classication through Enhancing Semantic Features—0
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering—0
EARLIN: Early Out-of-Distribution Detection for Resource-efficient Collaborative Inference—0
Dual Energy-Based Model with Open-World Uncertainty Estimation for Out-of-distribution Detection—0
Multidimensional Uncertainty Quantification for Deep Neural Networks—0
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