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arXiv · 1806.02338

Towards Dependability Metrics for Neural Networks

Chih-Hong Cheng·Georg Nührenberg·Chung-Hao Huang·Harald Ruess·Hirotoshi Yasuoka
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Abstract

Artificial neural networks (NN) are instrumental in realizing highly-automated driving functionality. An overarching challenge is to identify best safety engineering practices for NN and other learning-enabled components. In particular, there is an urgent need for an adequate set of metrics for measuring all-important NN dependability attributes. We address this challenge by proposing a number of NN-specific and efficiently computable metrics for measuring NN dependability attributes including robustness, interpretability, completeness, and correctness.

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Chih-Hong Cheng, Georg Nührenberg, Chung-Hao Huang, Harald Ruess, Hirotoshi Yasuoka. 2018-06-06. Towards Dependability Metrics for Neural Networks. https://arxiv.org/abs/1806.02338

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