Summary of Transient Fault Tolerant Semantic Segmentation For Autonomous Driving, by Leonardo Iurada et al.
Transient Fault Tolerant Semantic Segmentation for Autonomous Driving
by Leonardo Iurada, Niccolò Cavagnero, Fernando Fernandes Dos Santos, Giuseppe Averta, Paolo Rech, Tatiana Tommasi
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 research paper tackles the challenge of ensuring deep learning models for autonomous vehicle perception remain reliable in the face of hardware faults. The authors investigate fault-tolerance in semantic segmentation models, evaluating existing techniques for both accuracy and uncertainty. They introduce ReLUMax, a simple yet effective activation function designed to enhance resilience against transient faults. This novel approach integrates seamlessly into existing architectures without adding processing overhead. Experiments demonstrate that ReLUMax improves robustness, preserving performance while boosting prediction confidence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models are essential for self-driving cars to recognize objects on the road. But what happens when these systems fail due to technical glitches or equipment malfunctions? Researchers want to make sure they can still rely on their algorithms even if something goes wrong. They looked at how well current methods work against hardware faults and came up with a new way called ReLUMax. This innovation is easy to add to existing models without slowing them down. The results show that ReLUMax makes these systems more robust, so they can still make good decisions even if something goes wrong. |
Keywords
» Artificial intelligence » Boosting » Deep learning » Semantic segmentation
