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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 321–330 of 629 papers

TitleStatusHype
Out-of-distribution detection for regression tasks: parameter versus predictor entropy—0
PAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection—0
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training—0
Improving Out-of-Distribution Detection with Markov Logic Networks—0
Improving Training and Inference of Face Recognition Models via Random Temperature Scaling—0
Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks—0
Informative Outlier Matters: Robustifying Out-of-distribution Detection Using Outlier Mining—0
Instance-Aware Observer Network for Out-of-Distribution Object Segmentation—0
Interpretable Out-Of-Distribution Detection Using Pattern Identification—0
Interpreting deep learning output for out-of-distribution detection—0
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