Gradient-Based Interpretability Methods and Binarized Neural Networks
This work addresses the problem of interpretability reliability for BNNs in edge computing, but it is incremental as it primarily compares existing methods without introducing new techniques.
The paper assessed the performance of gradient-based interpretability methods on Binarized Neural Networks (BNNs) compared to Full Precision Neural Networks, finding that methods like SmoothGrad produce noisier maps and GradCAM yields nonsensical explanations for BNNs, highlighting differences in explanation quality.
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.