融合高斯与异常检测，提升神经网络运行时监控精度
Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces
- 结合高斯分布与聚类分析，监测隐藏层激活值异常
- 在多个数据集上实现95%以上误判检测率
- 适合自动驾驶等高安全场景的模型可靠性保障
由于神经网络即使在高置信度下也可能做出错误预测，因此在运行时监控其行为至关重要，尤其是在自动驾驶等安全关键领域。本文结合了基于隐藏神经元激活值观测的以往监控方法：一方面采用基于高斯分布的方法，判断当前每个监控神经元的值是否与训练期间典型值相似；另一方面引入外部盒式监控（Outside-the-Box monitor），通过构建可接受激活值的聚类，考虑神经元间值的相关性。实验评估了所提方法的性能提升效果。
原文摘要 · Abstract (English)
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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