Summary of Cood: Combined Out-of-distribution Detection Using Multiple Measures For Anomaly & Novel Class Detection in Large-scale Hierarchical Classification, by L. E. Hogeweg et al.
COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification
by L. E. Hogeweg, R. Gangireddy, D. Brunink, V. J. Kalkman, L. Cornelissen, J.W. Kamminga
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to detecting out-of-distribution (OOD) images in large databases is proposed, combining individual OOD measures using a supervised model. This framework, called COOD, leverages both state-of-the-art and novel measures tailored for hierarchical class structures and severe class imbalance. Extensive evaluation on three large-scale biodiversity datasets demonstrates significant performance gains over individual OOD measures, with COOD achieving 85.4% TPR@1% FPR in detecting ImageNet images as OOD. SHAP analysis highlights the importance of diverse individual OOD measures for various tasks, suggesting that combining multiple measures is crucial for generalization. Furthermore, the study shows that incorporating incorrectly classified ID images is essential for constructing effective OOD detection methods and ensuring practical applicability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to detect pictures that don’t belong in a big database of images is presented. This method combines different ways of measuring when an image doesn’t belong with a special kind of machine learning model. The combined approach, called COOD, does better than individual methods at detecting out-of-distribution images. Tests on large datasets show that COOD works well and can even detect 85.4% of ImageNet images as not belonging in a database. This research also shows that different ways of measuring when an image doesn’t belong are important for different tasks, so combining multiple methods is key to making it work. Finally, the study highlights the importance of including mistakes made by the original classification model to make the OOD detection method more practical. |
Keywords
* Artificial intelligence * Classification * Generalization * Machine learning * Supervised
