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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 471–480 of 629 papers

TitleStatusHype
Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution DetectionCode0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
Out-Of-Distribution Detection In Unsupervised Continual Learning—0
Full-Spectrum Out-of-Distribution Detection—0
Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss—0
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography—0
Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem—0
Towards Textual Out-of-Domain Detection without In-Domain Labels—0
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space—0
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