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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 491–500 of 629 papers

TitleStatusHype
Revisit PCA-based Technique for Out-of-Distribution DetectionCode0
A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?Code0
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and AccuracyCode0
Robust Out-of-Distribution Detection on Deep Probabilistic Generative ModelsCode0
Distribution Calibration for Out-of-Domain Detection with Bayesian ApproximationCode0
Mining In-distribution Attributes in Outliers for Out-of-distribution DetectionCode0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
Robust Representation via Dynamic Feature AggregationCode0
Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based LearningCode0
VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution DetectionCode0
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