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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 191–200 of 629 papers

TitleStatusHype
RankFeat&RankWeight: Rank-1 Feature/Weight Removal for Out-of-distribution Detection—0
Towards Few-shot Out-of-Distribution Detection—0
Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data—0
Distilling the Unknown to Unveil CertaintyCode0
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning—0
Improvements on Uncertainty Quantification for Node Classification via Distance-Based RegularizationCode0
Out-of-distribution Detection Learning with Unreliable Out-of-distribution SourcesCode0
Detecting Out-of-Distribution Through the Lens of Neural CollapseCode0
Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos—0
Classifier-head Informed Feature Masking and Prototype-based Logit Smoothing for Out-of-Distribution Detection—0
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