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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 421–430 of 629 papers

TitleStatusHype
Exploiting Mixed Unlabeled Data for Detecting Samples of Seen and Unseen Out-of-Distribution Classes—0
OpenOOD: Benchmarking Generalized Out-of-Distribution DetectionCode0
How to Enable Uncertainty Estimation in Proximal Policy Optimization—0
A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural NetworksCode0
Out-of-Distribution Detection and Selective Generation for Conditional Language Models—0
Out-of-Distribution Detection for LiDAR-based 3D Object Detection—0
Raising the Bar on the Evaluation of Out-of-Distribution Detection—0
Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks—0
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection—0
Distribution Calibration for Out-of-Domain Detection with Bayesian ApproximationCode0
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