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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 251–300 of 629 papers

TitleStatusHype
Out-of-Distribution Detection by Leveraging Between-Layer Transformation SmoothnessCode0
Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion ImagesCode0
Out-of-distribution Detection in Classifiers via GenerationCode0
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing FlowsCode0
Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution DetectionCode0
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution DetectionCode0
Open-World Lifelong Graph LearningCode0
Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layerCode0
OpenOOD: Benchmarking Generalized Out-of-Distribution DetectionCode0
Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield EnergyCode0
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain DetectionCode0
On the Importance of Regularisation & Auxiliary Information in OOD DetectionCode0
On Out-of-Distribution Detection for Audio with Deep Nearest NeighborsCode0
On the detection of Out-Of-Distribution samples in Multiple Instance LearningCode0
On the Practicality of Deterministic Epistemic UncertaintyCode0
Non-Linear Outlier Synthesis for Out-of-Distribution DetectionCode0
Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generalization in OOD DetectionCode0
No True State-of-the-Art? OOD Detection Methods are Inconsistent across DatasetsCode0
NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal VisionCode0
Enhancing OOD Detection Using Latent DiffusionCode0
Do Bayesian Variational Autoencoders Know What They Don't Know?Code0
Kernel PCA for Out-of-Distribution DetectionCode0
Multi-Label Out-of-Distribution Detection with Spectral Normalized Joint EnergyCode0
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and AccuracyCode0
ITP: Instance-Aware Test Pruning for Out-of-Distribution DetectionCode0
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?Code0
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
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain DetectionCode0
Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented DialogCode0
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
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
Improvements on Uncertainty Quantification for Node Classification via Distance-Based RegularizationCode0
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
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