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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 381–390 of 629 papers

TitleStatusHype
Uncertainty-Estimation with Normalized Logits for Out-of-Distribution Detection—0
Two-step counterfactual generation for OOD examples—0
Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection—0
Cluster-aware Contrastive Learning for Unsupervised Out-of-distribution Detection—0
Uncertainty-Aware Multiple-Instance Learning for Reliable Classification: Application to Optical Coherence Tomography—0
Plugin estimators for selective classification with out-of-distribution detection—0
Interpretable Out-Of-Distribution Detection Using Pattern Identification—0
Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield EnergyCode0
Free Lunch for Generating Effective Outlier Supervision—0
Revisit PCA-based Technique for Out-of-Distribution DetectionCode0
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