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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 481–490 of 629 papers

TitleStatusHype
Igeood: An Information Geometry Approach to Out-of-Distribution DetectionCode0
Concept-based Explanations for Out-Of-Distribution DetectorsCode0
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
Out of Distribution Detection on ImageNet-OCode0
Self-Supervised Anomaly Detection by Self-Distillation and Negative SamplingCode0
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection—0
Deep Hybrid Models for Out-of-Distribution Detection—0
Boundary Aware Learning for Out-of-distribution Detection—0
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