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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 431–440 of 629 papers

TitleStatusHype
Detecting Out-of-distribution Examples via Class-conditional Impressions Reappearing—0
Out-of-Distribution Detection with Reconstruction Error and Typicality-based Penalty—0
A Variational Information Theoretic Approach to Out-of-Distribution Detection—0
A Unified Approach Towards Active Learning and Out-of-Distribution Detection—0
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing—0
Out-of-Distribution Detection with Class Ratio Estimation—0
Detecting Anomalous Event Sequences with Temporal Point Processes—0
Out-of-Distribution Detection with Overlap Index—0
Detecting and Learning Out-of-Distribution Data in the Open world: Algorithm and Theory—0
Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features—0
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