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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 231–240 of 629 papers

TitleStatusHype
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment—0
Detecting Anomalous Event Sequences with Temporal Point Processes—0
A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space—0
Informative Outlier Matters: Robustifying Out-of-distribution Detection Using Outlier Mining—0
Interpreting deep learning output for out-of-distribution detection—0
Graph Synthetic Out-of-Distribution Exposure with Large Language Models—0
GRODIN: Improved Large-Scale Out-of-Domain detection via Back-propagation—0
GROOD: Gradient-Aware Out-of-Distribution Detection—0
kFolden: k-Fold Ensemble for Out-Of-Distribution Detection—0
Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification—0
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