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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 276–300 of 629 papers

TitleStatusHype
On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution DetectionCode0
Mining In-distribution Attributes in Outliers for Out-of-distribution DetectionCode0
Distribution Calibration for Out-of-Domain Detection with Bayesian ApproximationCode0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
Are Bayesian neural networks intrinsically good at out-of-distribution detection?Code0
Input complexity and out-of-distribution detection with likelihood-based generative modelsCode0
Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time ApplicationsCode0
Long-Tailed Out-of-Distribution Detection: Prioritizing Attention to TailCode0
Improving Out-of-Distribution Detection by Combining Existing Post-hoc MethodsCode0
Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented DialogCode0
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain DetectionCode0
Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution AdaptationCode0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based LearningCode0
Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural NetworksCode0
Improvements on Uncertainty Quantification for Node Classification via Distance-Based RegularizationCode0
Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selections and ApproximationsCode0
Key Feature Replacement of In-Distribution Samples for Out-of-Distribution DetectionCode0
kFolden: k-Fold Ensemble for Out-Of-Distribution DetectionCode0
Semi-supervised novelty detection using ensembles with regularized disagreementCode0
Adversarial Self-Supervised Learning for Out-of-Domain DetectionCode0
Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution DetectionCode0
ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection AlgorithmsCode0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
Igeood: An Information Geometry Approach to Out-of-Distribution DetectionCode0
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