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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 376–400 of 629 papers

TitleStatusHype
Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent ThresholdCode0
Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion ImagesCode0
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering—0
SHELS: Exclusive Feature Sets for Novelty Detection and Continual Learning Without Class BoundariesCode0
POEM: Out-of-Distribution Detection with Posterior SamplingCode1
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection—0
WeShort: Out-of-distribution Detection With Weak Shortcut structure—0
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective—0
Multiple Testing Framework for Out-of-Distribution Detection—0
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core QuantitiesCode1
Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space—0
Supervision Adaptation Balancing In-distribution Generalization and Out-of-distribution Detection—0
READ: Aggregating Reconstruction Error into Out-of-distribution Detection—0
Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution DetectionCode1
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
Accuracy on In-Domain Samples Matters When Building Out-of-Domain detectors: A Reply to Marek et al. (2021)Code1
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
Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution DetectionCode0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
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