Summary of Evaluation Of Out-of-distribution Detection Performance on Autonomous Driving Datasets, by Jens Henriksson et al.
Evaluation of Out-of-Distribution Detection Performance on Autonomous Driving Datasets
by Jens Henriksson, Christian Berger, Stig Ursing, Markus Borg
First submitted to arxiv on: 30 Jan 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 Medium Difficulty summary: This paper investigates the evaluation methods used in Deep Neural Networks (DNNs) for critical applications. The authors highlight the need for systematic investigation into safety measures to ensure DNNs perform as intended. They identify a lack of verification methods for high-dimensional DNNs, leading to a trade-off between accepted performance and handling out-of-distribution (OOD) samples. To address this issue, the paper proposes a framework that integrates OOD detection with robustness metrics. By doing so, it aims to provide a more comprehensive understanding of DNN performance in critical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper looks at how well Deep Neural Networks (DNNs) work for important tasks. The authors want to make sure these networks are safe and reliable. They found that there aren’t enough ways to test high-dimensional DNNs, which means we have to choose between getting good results or being able to handle unexpected data. To fix this problem, the paper suggests a new way of checking how well DNNs do in different situations. |
