Summary of Adaptive Hierarchical Certification For Segmentation Using Randomized Smoothing, by Alaa Anani et al.
Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing
by Alaa Anani, Tobias Lorenz, Bernt Schiele, Mario Fritz
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This paper proposes a novel approach to machine learning certification, which is crucial for safety-critical domains. The authors address the issue of high abstain rates due to model uncertainty in segmentation tasks by introducing an adaptive hierarchical certification algorithm. This method certifies pixels within a multi-level hierarchy and relaxes the certification to a coarser level for unstable components, effectively reducing the abstain rate while providing more certified semantically meaningful information. The paper also introduces a new metric, Certified Information Gain (CIG), which takes into account the loss of information in coarser classes. Experimental results on several datasets demonstrate that the proposed algorithm outperforms current state-of-the-art certification methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research is about making sure machine learning models are accurate and reliable for important tasks like self-driving cars or medical diagnosis. The authors developed a new way to check if a model is good at certain tasks without getting confused by lots of different classes. This method helps reduce the number of times the model has to say “I’m not sure” about something, while still providing useful information. The results show that this approach works better than previous methods on various datasets. |
Keywords
* Artificial intelligence * Machine learning
