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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
Towards Unknown-aware Learning with Virtual Outlier Synthesis—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
Towards Unknown-aware Deep Q-Learning—0
Sneakoscope: Revisiting Unsupervised Out-of-Distribution Detection—0
Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty—0
TIME-LAPSE: Learning to say “I don't know” through spatio-temporal uncertainty scoring—0
DICE: A Simple Sparsification Method for Out-of-distribution Detection—0
Adversarial Distributions Against Out-of-Distribution Detectors—0
Can multi-label classification networks know what they don't know?Code1
DOODLER: Determining Out-Of-Distribution Likelihood from Encoder Reconstructions—0
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