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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 601–629 of 629 papers

TitleStatusHype
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram MatricesCode1
Why Should we Combine Training and Post-Training Methods for Out-of-Distribution Detection?—0
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection—0
Novelty Detection Via Blurring—0
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation—0
Out of distribution detection for intra-operative functional imaging—0
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer OutputCode0
Unsupervised Out-of-Distribution Detection with Batch Normalization—0
Toward Metrics for Differentiating Out-of-Distribution SetsCode0
Out-of-distribution Detection in Classifiers via GenerationCode0
Negative Sampling in Variational Autoencoders—0
Zero-Shot Out-of-Distribution Detection with Feature CorrelationsCode0
Neural Network Out-of-Distribution Detection for Regression Tasks—0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
Input complexity and out-of-distribution detection with likelihood-based generative modelsCode0
Out-of-domain Detection for Natural Language Understanding in Dialog SystemsCode1
Out-of-Domain Detection for Low-Resource Text Classification TasksCode0
Isotropy Maximization Loss and Entropic Score: Accurate, Fast, Efficient, Scalable, and Turnkey Neural Networks Out-of-Distribution Detection Based on The Principle of Maximum EntropyCode1
Unsupervised Out-of-Distribution Detection by Maximum Classifier DiscrepancyCode0
Detecting semantic anomaliesCode0
Out-of-Distribution Detection Using Neural Rendering Generative Models—0
Outlier Exposure with Confidence Control for Out-of-Distribution DetectionCode0
Likelihood Ratios for Out-of-Distribution DetectionCode1
Contextual Out-of-Domain Utterance Handling With Counterfeit Data AugmentationCode0
Analysis of Confident-Classifiers for Out-of-distribution DetectionCode0
Deep Anomaly Detection with Outlier ExposureCode1
WAIC, but Why? Generative Ensembles for Robust Anomaly DetectionCode0
Out-of-domain Detection based on Generative Adversarial Network—0
Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates—0
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