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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 211–220 of 629 papers

TitleStatusHype
OOD Aware Supervised Contrastive Learning—0
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?Code0
Scaling for Training Time and Post-hoc Out-of-distribution Detection EnhancementCode1
Dream the Impossible: Outlier Imagination with Diffusion ModelsCode1
Meta OOD Learning for Continuously Adaptive OOD Detection—0
On the detection of Out-Of-Distribution samples in Multiple Instance LearningCode0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
Unsupervised Out-of-Distribution Detection by Restoring Lossy Inputs with Variational AutoencoderCode0
Enhancing Automated and Early Detection of Alzheimer's Disease Using Out-Of-Distribution Detection—0
On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imagingCode1
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