Summary of A Stochastic Approach to Classification Error Estimates in Convolutional Neural Networks, by Jan Peleska et al.
A Stochastic Approach to Classification Error Estimates in Convolutional Neural Networks
by Jan Peleska, Felix Brüning, Mario Gleirscher, Wen-ling Huang
First submitted to arxiv on: 21 Dec 2023
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 This technical report presents research on verifying Convolutional Neural Networks (CNNs) used for image classification in safety-critical applications. The study focuses on obstacle detection in future autonomous freight trains with Grade of Automation (GoA) 4, using standards like ANSI/UL 4600, ISO 21448, EN 50128, and EN 50129. The report demonstrates that systems like GoA 4 freight trains are certifiable today using these standards. A quantitative analysis of the system-level hazard rate is also presented, showing that sensor/perceptor fusion can meet the tolerable hazard rate for SIL-3. Additionally, a mathematical analysis of CNN models identifies classification clusters and equivalence classes partitioning the image input space. These findings lead to a novel statistical testing method for determining the residual error probability of a trained CNN and an associated upper confidence limit. This greybox approach is essential for verifying CNNs by accounting for their internal structure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research report helps make sure that computer systems, like those used in self-driving trucks, are safe to use. The study looks at how well these systems can detect obstacles and prevent accidents. It shows that using special combinations of sensors and cameras can help ensure the safety of these systems. The report also introduces a new way to test if these systems are working correctly. This approach considers the internal workings of the computer system, making it more reliable for verifying its performance. |
Keywords
* Artificial intelligence * Classification * Cnn * Image classification * Probability
