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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 171–180 of 629 papers

TitleStatusHype
A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection—0
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing—0
Detecting Anomalous Event Sequences with Temporal Point Processes—0
Beyond Mahalanobis-Based Scores for Textual OOD Detection—0
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
Detecting and Learning Out-of-Distribution Data in the Open world: Algorithm and Theory—0
Density of States Estimation for Out-of-Distribution Detection—0
Benchmarking Post-Hoc Unknown-Category Detection in Food Recognition—0
VRA: Variational Rectified Activation for Out-of-distribution Detection—0
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
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