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We present a novel logic-based concept called Space Explanations for classifying neural networks that gives provable guarantees of the behavior of the network in continuous areas of the input feature space. To automatically generate space explanations, we leverage a range of flexible Craig interpolation algorithms and unsatisfiable core generation. Based on real-life case studies, ranging from small to medium to large size, we demonstrate that the generated explanations are more meaningful than those computed by state-of-the-art.

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Space Explanations of Neural Network Classification

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Faezeh Labbaf,
  • Tomáš Kolárik,
  • Martin Blicha,
  • Grigory Fedyukovich,
  • Michael Wand,
  • Natasha Sharygina

摘要

We present a novel logic-based concept called Space Explanations for classifying neural networks that gives provable guarantees of the behavior of the network in continuous areas of the input feature space. To automatically generate space explanations, we leverage a range of flexible Craig interpolation algorithms and unsatisfiable core generation. Based on real-life case studies, ranging from small to medium to large size, we demonstrate that the generated explanations are more meaningful than those computed by state-of-the-art.