Binarized Neural Networks (BNNs) have the potential to revolutionize the way that deep learning is carried out in edge computing platforms. However, the effectiveness of interpretability methods on these networks has not been assessed. In this paper, we compare the performance of several widely used saliency map-based interpretabilty techniques (Gradient, SmoothGrad and GradCAM), when applied to Binarized or Full Precision Neural Networks (FPNNs). We found that the basic Gradient method produces very similar-looking maps for both types of network. However, SmoothGrad produces significantly noisier maps for BNNs. GradCAM also produces saliency maps which differ between network types, with some of the BNNs having seemingly nonsensical explanations. We comment on possible reasons for these differences in explanations and present it as an example of why interpretability techniques should be tested on a wider range of network types.

翻译：催化神经网络(BNNS)有可能使边端计算平台深层学习的方式发生革命性的变化,然而,尚未对这些网络的可解释性方法的有效性进行评估。在本文件中,我们比较了在应用到催化或全面精密神经网络(PFNS)时广泛使用的几种基于地图的显性解析性技术(GradGrad和GradCAM)的性能。我们发现,基本渐变法为这两种网络制作了非常相似的地图。然而,SplyGrad为BNS制作了显著的隐喻地图。GradCAM还绘制了不同网络类型之间的显性地图,而一些BNNS似乎没有解释。我们评论了这些解释上的差异的可能原因,并将其作为一个实例说明为什么解释性技术应该用更广泛的网络类型进行测试。