# Strategic Input Selection for Deep Neural Networks
## Reliability Evaluation

Lucas Roquet, Fernando Fernandes dos Santos, Maria Kastriotou, Angeliki Kritikakou

► To cite this version:

Lucas Roquet, Fernando Fernandes dos Santos, Maria Kastriotou, Angeliki Kritikakou. Strategic Input Selection for Deep Neural Networks Reliability Evaluation. NSREC 2025 - IEEE Nuclear & Space Radiation Effects Conference, Jul 2025, Nashville (Tennessee), United States. 2025. hal-05273538

HAL Id: hal-05273538

https://hal.science/hal-05273538v1

Submitted on 23 Sep 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Strategic Input Selection for Deep Neural Networks Reliability Evaluation

Lucas Roquet,¹ Fernando Fernandes dos Santos,¹ Maria Kastriotou,² and Angeliki Kritikakou¹

¹Univ Rennes, CNRS, Inria, IRISA - UMR 6074, F-35000 Rennes, France

²ISIS Neutron and Muon Source, United Kingdom

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## Main Takeaways:

* Random input sets show much lower misclassification rates than low confidence

* Similar trends were observed under beam experiments and software fault simulation

## Future works:

* Explore other input selection metrics

* Are these trends similar for other tasks like text or video processing?