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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 161–170 of 629 papers

TitleStatusHype
Detecting semantic anomaliesCode0
Detecting Out-of-Distribution Through the Lens of Neural CollapseCode0
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer OutputCode0
Boosting Out-of-Distribution Detection with Multiple Pre-trained ModelsCode0
A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution DetectionCode0
Detecting Out-of-distribution Data through In-distribution Class PriorCode0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?Code0
Mining In-distribution Attributes in Outliers for Out-of-distribution DetectionCode0
Long-Tailed Out-of-Distribution Detection: Prioritizing Attention to TailCode0
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