Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces

Vahid Hashemi; Jan Křetínský; Sabine Rieder; Torsten Schön; Jan Vorhoff

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arXiv · 2410.06051

Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces

Vahid Hashemi·Jan Křetínský·Sabine Rieder·Torsten Schön·Jan Vorhoff

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Abstract

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.

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Vahid Hashemi, Jan Křetínský, Sabine Rieder, Torsten Schön, Jan Vorhoff. 2024-10-08. Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces. https://arxiv.org/abs/2410.06051

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