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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 461–470 of 629 papers

TitleStatusHype
Supervision Adaptation Balancing In-distribution Generalization and Out-of-distribution Detection—0
READ: Aggregating Reconstruction Error into Out-of-distribution Detection—0
Federated Learning with Uncertainty via Distilled Predictive Distributions—0
Out-of-Distribution Detection with Class Ratio Estimation—0
What do we learn? Debunking the Myth of Unsupervised Outlier Detection—0
Transformer-based out-of-distribution detection for clinically safe segmentationCode0
How Useful are Gradients for OOD Detection Really?—0
Robust Representation via Dynamic Feature AggregationCode0
KNN-Contrastive Learning for Out-of-Domain Intent Classification—0
Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification—0
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