Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces
<|reference_start|>Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces: Since neural networks can make wrong predictions even with high confidence, monitoring their behavior at runtime is important, especially in safety-critical domains like autonomous driving. In this paper, we combine ideas from previous monitoring approaches based on observing the activation values of hidden neurons. In particular, we combine the Gaussian-based approach, which observes whether the current value of each monitored neuron is similar to typical values observed during training, and the Outside-the-Box monitor, which creates clusters of the acceptable activation values, and, thus, considers the correlations of the neurons' values. Our experiments evaluate the achieved improvement.<|reference_end|>
@article{hashemi2024gaussian-based,
    title={Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces},
    author={Vahid Hashemi, Jan Kv{r}et'insk'y, Sabine Rieder, Torsten Sch"on
  and Jan Vorhoff},
    journal={arXiv preprint arXiv:2410.06051},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06051},
    primaryClass={cs.LG}
}