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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 521–530 of 629 papers

TitleStatusHype
Revisiting Out-of-Distribution Detection: A Simple Baseline is Surprisingly Effective—0
Intra-class Mixup for Out-of-Distribution Detection—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
Adversarial Distributions Against Out-of-Distribution Detectors—0
Sneakoscope: Revisiting Unsupervised Out-of-Distribution Detection—0
GRODIN: Improved Large-Scale Out-of-Domain detection via Back-propagation—0
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty—0
DICE: A Simple Sparsification Method for Out-of-distribution Detection—0
TIME-LAPSE: Learning to say “I don't know” through spatio-temporal uncertainty scoring—0
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