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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 401–410 of 629 papers

TitleStatusHype
Out-Of-Distribution Detection In Unsupervised Continual Learning—0
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human FeedbackCode2
Full-Spectrum Out-of-Distribution Detection—0
Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss—0
RODD: A Self-Supervised Approach for Robust Out-of-Distribution DetectionCode1
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
Continual Learning Based on OOD Detection and Task MaskingCode1
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