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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 411–420 of 629 papers

TitleStatusHype
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space—0
Igeood: An Information Geometry Approach to Out-of-Distribution DetectionCode0
Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the WildCode1
How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?Code1
Concept-based Explanations for Out-Of-Distribution DetectorsCode0
MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasksCode1
Layer Adaptive Deep Neural Networks for Out-of-distribution DetectionCode0
Computer Aided Diagnosis and Out-of-Distribution Detection in Glaucoma Screening Using Color Fundus Photography—0
Model2Detector:Widening the Information Bottleneck for Out-of-Distribution Detection using a Handful of Gradient Steps—0
Agree to Disagree: Diversity through Disagreement for Better TransferabilityCode1
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