Summary of Continual Unsupervised Out-of-distribution Detection, by Lars Doorenbos et al.
Continual Unsupervised Out-of-Distribution Detection
by Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila
First submitted to arxiv on: 4 Jun 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 The proposed paper introduces a novel setting for deep learning models, called continual unsupervised out-of-distribution (U-OOD) detection. This setting reflects the real-world scenario where a model is deployed and must adapt to new, unseen data distributions. The authors propose a method that starts with a traditional U-OOD detector and updates it during deployment using a new scoring function combining Mahalanobis distance and nearest-neighbor approaches. Additionally, they design a confidence-scaled few-shot OOD detector that outperforms previous methods. This paper shows significant improvements over strong baselines in related fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way for deep learning models to deal with unknown data distributions when deployed. It’s like trying to recognize new faces you’ve never seen before! The authors make a special kind of model that gets better at recognizing these new “faces” as it sees more and more of them. They also create a new way to score how well the model does on this task, which seems to be very good. |
Keywords
» Artificial intelligence » Deep learning » Few shot » Nearest neighbor » Unsupervised
