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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 611–620 of 629 papers

TitleStatusHype
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
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