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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 461–470 of 629 papers

TitleStatusHype
Pseudo-OOD training for robust language models—0
Understanding Likelihood of Normalizing Flow and Image Complexity through the Lens of Out-of-Distribution Detection—0
The Hidden Uncertainty in a Neural Networks Activations—0
Quantum Conflict Measurement in Decision Making for Out-of-Distribution Detection—0
Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data—0
Raising the Bar on the Evaluation of Out-of-Distribution Detection—0
Random-Set Neural Networks (RS-NN)—0
RankFeat&RankWeight: Rank-1 Feature/Weight Removal for Out-of-distribution Detection—0
READ: Aggregating Reconstruction Error into Out-of-distribution Detection—0
Understanding Softmax Confidence and Uncertainty—0
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