OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples

One major cause of performance degradation in predictive models is that the test samples are not well covered by the training data. Such not well-represented samples are called OoD samples. In this article, we propose OoDAnalyzer, a visual analysis approach for interactively identifying OoD samples...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2021-07, Vol.27 (7), p.3335-3349
Main Authors: Chen, Changjian, Yuan, Jun, Lu, Yafeng, Liu, Yang, Su, Hang, Yuan, Songtao, Liu, Shixia
Format: Article
Language:English
Subjects:
Algorithms
Approximation algorithms
Cats
Complexity
Context
Dogs
Feature extraction
grid layout
interactive visualization
Layout
Layouts
Out-of-Distribution detection
Performance degradation
Performance prediction
Prediction models
Training
Visualization
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